Datasets:

modelomics
/

gh-ml

@@ -105,9 +105,9 @@ The configured workflow runs four Search passes with a default total budget of 2
 The `historical-sample` pass takes one ranked first-page search for every query/year lane from 2008 through a campaign end date fixed when the campaign starts. That end date stays fixed across partial runs and later runs with an expanded catalog. Its v2 completion ledger preserves completed `(query ID, query text, year)` lanes across catalog additions and edits. New or changed queries are sampled for every year in the fixed campaign, removed queries are dropped, and a legacy v1 cursor is safely migrated by matching query signatures. The checkpoint is `state/historical-sample.json` on the Hub and `historical-sample-state.json` in local output. Completed ledger entries persist, so future catalog additions resume only their missing lanes. The bounded request budget remains 200 per scheduled day, processed round-robin across query groups. These annual samples can improve breadth across creation years, but ranked first-page sampling can omit matching repositories; they do not replace historical backfill or establish completeness. The scheduled `backfill-fair` pass rotates through query/date-partition work, issuing one Search request per query in each rotation to spread its bounded budget across queries. Its checkpoints are `state/backfill-fair.json` on the Hub and `backfill-fair-state.json` in local output. The legacy `backfill` command still uses `state/backfill.json` / `backfill-state.json`; fair backfill uses a separate checkpoint and does not migrate the old cursor. Do not claim exhaustive coverage until all date partitions have been scanned and coverage records show a completed sweep. Recent and breadth checkpoints remain at `state/checkpoint.json` and `state/sample.json`, respectively. Within a run, matches merge by numeric `github_id`, never by mutable `name`. Across runs, the observation history is append-only; `uv run gh-ml current-view /path/to/downloaded-dataset --output ~/.local/share/modelomics-gh-ml/current-view.jsonl` builds one row per ID from local `data/observations/**/*.jsonl` files. It prefers Search observations over topic and census observations, then chooses the greatest `observed_at` within that source. Any `all_*` accumulated-label fields retain values across history while ordinary fields come from the chosen row. This local command does not load separate README evidence files; the published snapshot publisher joins available README evidence before selection. The command writes a manifest and does not modify the Hub. For Parquet output, install `uv sync --extra parquet` and provide `--parquet-output <path>`.
-The current projection prefers Search observations over topic and census observations for each GitHub ID, then selects the greatest `observed_at` within that source. It adds `first_observed_at`, `observation_count`, and sorted unions of labels, including `paper_ids`, then evaluates repository-owned metadata and any available compact README signals with `ml-contribution-v5`. Inclusion can come from a method-specific novelty claim with a recognized ML method cue, description text linking an official/authors’ paper and implementation to ML method evidence, or compact README signals that support an ML method contribution with a paper/code relationship or official implementation claim. The `candidates` view contains every included row plus review rows with ML method and text evidence, a nonempty description, and either a paper-and-code cue or an associated `paper_ids` value (`ml-candidate-v3`). A Daily Papers link can therefore make a qualified review row eligible for `candidates`, but cannot cause inclusion in `current`; the association is unverified provenance, not proof of an official implementation or novelty. Generic contribution language alone does not qualify. Overview, reproduction, and dataset cues lead to review rather than inclusion on their own; tutorials, surveys, coursework, forks, profiles, and unrelated utilities are screened out, while a utility with a method-specific novelty cue may remain for review. None of these cases alone establishes a distinct contribution or novelty. Only `include` rows enter `current`; `review` rows enter `candidates`; `observations` preserves every unfiltered raw observation. The selector uses repository-owned metadata and, when available, extracted README signals, not query labels, to decide. README text is processed in memory and never stored; only bounded enum signals, section names, content hash, ETag, status, and timestamps are published. The README pass prioritizes previously selected include and review rows, applies a 150-request daily maximum, and stores progress in `state/readme-evidence.json`; evidence is refreshed according to its checkpoint and is not guaranteed to cover every candidate. README extraction is heuristic and can miss relevant evidence or misread repository claims. The Parquet `observation_count` counts raw historical observations for each repository, and the manifest reports raw rows, latest repositories, and selection counts. This heuristic cannot verify claims or guarantee novelty; false positives and missed candidates remain possible.
-Selector v5 routes exploration or comparison of named existing time-series models to review, even when a repository name says “novel model.” A direct novelty claim with an active README coursework signal also stays in review when the active README has a coursework cue without a paper reference or official/paper-code signal; a paper reference is provenance, not proof of officiality or novelty.
 The source Search catalog currently contains 584 queries (569 existing plus 15 additions). It is intentionally broad and serves only as retrieval provenance for the raw `observations` config. Query-derived methods or domains cannot satisfy the contribution selector. All records are keyed by numeric `github_id`. The scheduled `census-daily` collector independently enumerates bounded pages from GitHub Core `/repositories`; the README evidence pass remains a distinct Core API enrichment for selected repositories. Search observations take precedence over topic or census observations for IDs seen in those streams, before the strict selector builds the included `current` and broader `candidates` views. The [census guide](../docs/census.md) and [topic breadth guide](../docs/topic-breadth.md) describe these queryless sources, their state, and limits. The configured topic pass uses GraphQL over an ordered 38-slug catalog, with up to 68 GraphQL pages per daily run: a first-page refresh for each of 38 topics and up to 30 deeper cursor pages. The eight added field topics cover geospatial, remote sensing, bioinformatics, cheminformatics, speech recognition, text to speech, medical imaging, and recommender systems. Completed deep sweeps become eligible to restart after 30 days, while first-page refreshes continue daily; no completeness or snapshot-isolation guarantee is made. Topic collector output and workflow configuration do not establish that a remote run has executed or published.

 The `historical-sample` pass takes one ranked first-page search for every query/year lane from 2008 through a campaign end date fixed when the campaign starts. That end date stays fixed across partial runs and later runs with an expanded catalog. Its v2 completion ledger preserves completed `(query ID, query text, year)` lanes across catalog additions and edits. New or changed queries are sampled for every year in the fixed campaign, removed queries are dropped, and a legacy v1 cursor is safely migrated by matching query signatures. The checkpoint is `state/historical-sample.json` on the Hub and `historical-sample-state.json` in local output. Completed ledger entries persist, so future catalog additions resume only their missing lanes. The bounded request budget remains 200 per scheduled day, processed round-robin across query groups. These annual samples can improve breadth across creation years, but ranked first-page sampling can omit matching repositories; they do not replace historical backfill or establish completeness. The scheduled `backfill-fair` pass rotates through query/date-partition work, issuing one Search request per query in each rotation to spread its bounded budget across queries. Its checkpoints are `state/backfill-fair.json` on the Hub and `backfill-fair-state.json` in local output. The legacy `backfill` command still uses `state/backfill.json` / `backfill-state.json`; fair backfill uses a separate checkpoint and does not migrate the old cursor. Do not claim exhaustive coverage until all date partitions have been scanned and coverage records show a completed sweep. Recent and breadth checkpoints remain at `state/checkpoint.json` and `state/sample.json`, respectively. Within a run, matches merge by numeric `github_id`, never by mutable `name`. Across runs, the observation history is append-only; `uv run gh-ml current-view /path/to/downloaded-dataset --output ~/.local/share/modelomics-gh-ml/current-view.jsonl` builds one row per ID from local `data/observations/**/*.jsonl` files. It prefers Search observations over topic and census observations, then chooses the greatest `observed_at` within that source. Any `all_*` accumulated-label fields retain values across history while ordinary fields come from the chosen row. This local command does not load separate README evidence files; the published snapshot publisher joins available README evidence before selection. The command writes a manifest and does not modify the Hub. For Parquet output, install `uv sync --extra parquet` and provide `--parquet-output <path>`.
+The current projection prefers Search observations over topic and census observations for each GitHub ID, then selects the greatest `observed_at` within that source. It adds `first_observed_at`, `observation_count`, and sorted unions of labels, including `paper_ids`, then evaluates repository-owned metadata and any available compact README signals with `ml-contribution-v5`. Inclusion can come from a method-specific novelty claim with a recognized ML method cue, description text linking an official/authors’ paper and implementation to ML method evidence, or compact README signals that support an ML method contribution with a paper/code relationship or official implementation claim. The `candidates` view contains every included row plus review rows with ML method and text evidence, a nonempty description, and either a paper-and-code cue or an associated `paper_ids` value (`ml-candidate-v3`). A Daily Papers link can therefore make a qualified review row eligible for `candidates`, but cannot cause inclusion in `current`; the association is unverified provenance, not proof of an official implementation or novelty. Generic contribution language alone does not qualify. Overview, reproduction, and dataset cues lead to review rather than inclusion on their own; educational cues such as course/coursework, class or course projects, homework, and assignments are screened as coursework, while a bare mention of a class, lecture, or tutorial is not sufficient. Surveys, forks, profiles, and unrelated utilities are screened out, while a utility with a method-specific novelty cue may remain for review. None of these cases alone establishes a distinct contribution or novelty. Only `include` rows enter `current`; `review` rows enter `candidates`; `observations` preserves every unfiltered raw observation. The selector uses repository-owned metadata and, when available, extracted README signals, not query labels, to decide. README text is processed in memory and never stored; only bounded enum signals, section names, content hash, ETag, status, and timestamps are published. The README pass prioritizes previously selected include and review rows, applies a 150-request daily maximum, and stores progress in `state/readme-evidence.json`; compact evidence with an older extraction version is eligible for gradual re-fetch within that same daily budget. Until fetched, old compact signals may remain attached and can still affect selection, so published rows are not all corrected immediately. Evidence refresh is not guaranteed to cover every candidate. README extraction is heuristic and can miss relevant evidence or misread repository claims. The Parquet `observation_count` counts raw historical observations for each repository, and the manifest reports raw rows, latest repositories, and selection counts. This heuristic cannot verify claims or guarantee novelty; false positives and missed candidates remain possible.
+Selector v5 routes exploration or comparison of named existing time-series models to review, even when a repository name says “novel model.” README extraction v2 uses specific educational wording (course/coursework, class or course projects, homework, and assignments); bare mentions of “class,” “lecture,” or “tutorial” do not create a coursework signal. A direct novelty claim with an active README educational signal also stays in review when the active README has that cue without a paper reference or official/paper-code signal; a paper reference is provenance, not proof of officiality or novelty. Stale compact README evidence is re-fetched gradually under the existing 150-request daily cap. Until a repository is fetched, its older compact signal may remain in the published projection.
 The source Search catalog currently contains 584 queries (569 existing plus 15 additions). It is intentionally broad and serves only as retrieval provenance for the raw `observations` config. Query-derived methods or domains cannot satisfy the contribution selector. All records are keyed by numeric `github_id`. The scheduled `census-daily` collector independently enumerates bounded pages from GitHub Core `/repositories`; the README evidence pass remains a distinct Core API enrichment for selected repositories. Search observations take precedence over topic or census observations for IDs seen in those streams, before the strict selector builds the included `current` and broader `candidates` views. The [census guide](../docs/census.md) and [topic breadth guide](../docs/topic-breadth.md) describe these queryless sources, their state, and limits. The configured topic pass uses GraphQL over an ordered 38-slug catalog, with up to 68 GraphQL pages per daily run: a first-page refresh for each of 38 topics and up to 30 deeper cursor pages. The eight added field topics cover geospatial, remote sensing, bioinformatics, cheminformatics, speech recognition, text to speech, medical imaging, and recommender systems. Completed deep sweeps become eligible to restart after 30 days, while first-page refreshes continue daily; no completeness or snapshot-isolation guarantee is made. Topic collector output and workflow configuration do not establish that a remote run has executed or published.

coverage/20260926T134635000000Z-22372881.json ADDED Viewed

	@@ -0,0 +1,1065 @@

+{
+  "complete_sweep": true,
+  "date_field": "created",
+  "mode": "sample",
+  "queries": [
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"time series classification\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.classification",
+      "repositories_observed_on_last_page": 0,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 0
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"time series clustering\" in:description,readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.clustering",
+      "repositories_observed_on_last_page": 1,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 1
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"energy forecasting\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.energy-forecasting",
+      "repositories_observed_on_last_page": 2,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 2
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"event forecasting\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.event-forecasting",
+      "repositories_observed_on_last_page": 0,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 0
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"time series forecasting\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.forecasting",
+      "repositories_observed_on_last_page": 19,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 19
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"time series foundation model\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.foundation-models",
+      "repositories_observed_on_last_page": 3,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 3
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"time series imputation\" in:description,readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.imputation",
+      "repositories_observed_on_last_page": 1,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 1
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"long horizon forecasting\" in:readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.long-horizon",
+      "repositories_observed_on_last_page": 4,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 4
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"multivariate time series\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.multivariate",
+      "repositories_observed_on_last_page": 1,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 1
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "N-BEATS in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.nbeats",
+      "repositories_observed_on_last_page": 0,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 0
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "PatchTST in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.patchtst",
+      "repositories_observed_on_last_page": 1,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 1
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"predictive maintenance\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.predictive-maintenance",
+      "repositories_observed_on_last_page": 35,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 35
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"probabilistic forecasting\" in:readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.probabilistic-forecasting",
+      "repositories_observed_on_last_page": 16,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 16
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"remaining useful life\" in:readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.remaining-useful-life",
+      "repositories_observed_on_last_page": 38,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 38
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"sensor time series\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.sensor-data",
+      "repositories_observed_on_last_page": 3,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 3
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"state space model\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.state-space-models",
+      "repositories_observed_on_last_page": 0,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 0
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"streaming time series\" in:readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.streaming",
+      "repositories_observed_on_last_page": 4,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 4
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"time series generation\" in:description,readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.synthetic-generation",
+      "repositories_observed_on_last_page": 4,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 4
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"tabular foundation model\" in:description,readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.tabular-foundation-models",
+      "repositories_observed_on_last_page": 5,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 5
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"temporal fusion transformer\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.temporal-fusion-transformer",
+      "repositories_observed_on_last_page": 0,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 0
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"temporal representation learning\" in:readme created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.temporal-representation",
+      "repositories_observed_on_last_page": 1,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 1
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "topic:time-series created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.topic",
+      "repositories_observed_on_last_page": 20,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 20
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"traffic forecasting\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.traffic-forecasting",
+      "repositories_observed_on_last_page": 1,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 1
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"irregular time series\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.unevenly-spaced",
+      "repositories_observed_on_last_page": 0,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 0
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"weather forecasting\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "timeseries.weather-forecasting",
+      "repositories_observed_on_last_page": 21,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 21
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"adversarial robustness\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "trust.adversarial-robustness",
+      "repositories_observed_on_last_page": 5,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 5
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"AI alignment\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "trust.ai-alignment",
+      "repositories_observed_on_last_page": 1,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 1
+    },
+    {
+      "coverage_gap": false,
+      "coverage_gap_reason": null,
+      "incomplete_results": false,
+      "pages_scanned": 1,
+      "query": "\"AI safety\" in:description created:2026-09-24..2026-09-24",
+      "query_id": "trust.ai-safety",
+      "repositories_observed_on_last_page": 12,
+      "sampled_first_page": true,
+      "search_limit_reached": false,
+      "status": "complete",
+      "total_count": 12
+    },
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data/observations/2026/09/26/20260926T134635000000Z-22372881.jsonl ADDED Viewed

The diff for this file is too large to render. See raw diff

state/sample.json CHANGED Viewed

@@ -1,2352 +1,7 @@
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-      {
-        "id": "gap2026.forecasting-hierarchical",
-        "query": "\"hierarchical time series forecasting\" machine learning in:description,readme"
-      },
-      {
-        "id": "gap2026.forecasting-intermittent-demand",
-        "query": "\"intermittent demand forecasting\" machine learning in:description,readme"
-      },
-      {
-        "id": "gap2026.forecasting-quantile",
-        "query": "\"neural quantile forecasting\" in:description,readme"
-      },
-      {
-        "id": "gap2026.green-ai-carbon",
-        "query": "\"carbon emissions\" machine learning training in:description,readme"
-      },
-      {
-        "id": "gap2026.green-ai-carbon-aware-scheduling",
-        "query": "\"carbon-aware\" machine learning scheduling in:description,readme"
-      },
-      {
-        "id": "gap2026.green-ai-efficient-inference",
-        "query": "\"energy efficient inference\" neural network in:description,readme"
-      },
-      {
-        "id": "gap2026.green-ai-energy",
-        "query": "\"energy efficient machine learning\" in:description,readme"
-      },
-      {
-        "id": "gap2026.math-learned-optimization",
-        "query": "\"learned optimizer\" optimization algorithm in:description,readme"
-      },
-      {
-        "id": "gap2026.math-neural-combinatorial-optimization",
-        "query": "\"neural combinatorial optimization\" in:description,readme"
-      },
-      {
-        "id": "gap2026.neuroimaging-connectome",
-        "query": "\"connectome prediction\" machine learning in:description,readme"
-      },
-      {
-        "id": "gap2026.neuroimaging-eeg",
-        "query": "\"EEG deep learning\" in:description,readme"
-      },
-      {
-        "id": "gap2026.neuroimaging-fmri",
-        "query": "\"fMRI machine learning\" in:description,readme"
-      },
-      {
-        "id": "gap2026.neuroimaging-neural-decoding",
-        "query": "\"neural decoding\" machine learning in:description,readme"
-      },
-      {
-        "id": "gap2026.physics-differentiable-simulation",
-        "query": "\"differentiable physics simulation\" in:description,readme"
-      },
-      {
-        "id": "gap2026.physics-inverse-problem",
-        "query": "\"inverse problems\" neural network physics in:description,readme"
-      },
-      {
-        "id": "gap2026.physics-neural-pde-solver",
-        "query": "\"neural PDE solver\" in:description,readme"
-      },
-      {
-        "id": "gap2026.physics-quantum-ml",
-        "query": "\"quantum machine learning\" physics in:description,readme"
-      },
-      {
-        "id": "general.active-learning",
-        "query": "\"active learning\" in:description"
-      },
-      {
-        "id": "general.artificial-intelligence-description",
-        "query": "\"artificial intelligence\" in:description"
-      },
-      {
-        "id": "general.arxiv",
-        "query": "arxiv \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.automated-machine-learning",
-        "query": "\"automated machine learning\" in:readme"
-      },
-      {
-        "id": "general.bayesian-deep-learning",
-        "query": "\"Bayesian deep learning\" in:description,readme"
-      },
-      {
-        "id": "general.bayesian-optimization",
-        "query": "\"Bayesian optimization\" in:description"
-      },
-      {
-        "id": "general.clustering",
-        "query": "\"clustering algorithm\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.continual-learning",
-        "query": "\"continual learning\" in:description,readme"
-      },
-      {
-        "id": "general.contrastive-learning",
-        "query": "\"contrastive learning\" in:description,readme"
-      },
-      {
-        "id": "general.decision-trees",
-        "query": "\"decision tree\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.deep-learning-description",
-        "query": "\"deep learning\" in:description"
-      },
-      {
-        "id": "general.deep-learning-readme",
-        "query": "\"deep learning\" in:readme"
-      },
-      {
-        "id": "general.dimensionality-reduction",
-        "query": "\"dimensionality reduction\" in:description,readme"
-      },
-      {
-        "id": "general.domain-adaptation",
-        "query": "\"domain adaptation\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.gaussian-mixture-models",
-        "query": "\"Gaussian mixture model\" in:description,readme"
-      },
-      {
-        "id": "general.gaussian-process",
-        "query": "\"Gaussian process\" in:description"
-      },
-      {
-        "id": "general.gradient-boosting",
-        "query": "\"gradient boosting\" in:description"
-      },
-      {
-        "id": "general.hidden-markov-models",
-        "query": "\"hidden Markov model\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.logistic-regression",
-        "query": "\"logistic regression\" in:description,readme"
-      },
-      {
-        "id": "general.machine-learning-description",
-        "query": "\"machine learning\" in:description"
-      },
-      {
-        "id": "general.machine-learning-readme",
-        "query": "\"machine learning\" in:readme"
-      },
-      {
-        "id": "general.matrix-factorization",
-        "query": "\"matrix factorization\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.meta-learning",
-        "query": "\"meta-learning\" in:description,readme"
-      },
-      {
-        "id": "general.metric-learning",
-        "query": "\"metric learning\" in:description,readme"
-      },
-      {
-        "id": "general.model-architecture",
-        "query": "\"model architecture\" in:description"
-      },
-      {
-        "id": "general.multi-task-learning",
-        "query": "\"multi-task learning\" in:description,readme"
-      },
-      {
-        "id": "general.naive-bayes",
-        "query": "\"naive Bayes\" in:description,readme"
-      },
-      {
-        "id": "general.nearest-neighbors",
-        "query": "\"nearest neighbors\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.neural-network",
-        "query": "\"neural network\" in:description"
-      },
-      {
-        "id": "general.neural-processes",
-        "query": "\"neural processes\" in:description,readme"
-      },
-      {
-        "id": "general.normalizing-flows",
-        "query": "\"normalizing flows\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.novel-method",
-        "query": "\"novel method\" in:description"
-      },
-      {
-        "id": "general.paper-implementation",
-        "query": "\"paper implementation\" in:description"
-      },
-      {
-        "id": "general.probabilistic-programming",
-        "query": "\"probabilistic programming\" in:description,readme"
-      },
-      {
-        "id": "general.random-forest",
-        "query": "\"random forest\" in:description"
-      },
-      {
-        "id": "general.research-code",
-        "query": "\"research code\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "general.self-supervised-learning",
-        "query": "\"self-supervised learning\" in:description"
-      },
-      {
-        "id": "general.semi-supervised-learning",
-        "query": "\"semi-supervised learning\" in:description,readme"
-      },
-      {
-        "id": "general.support-vector-machine",
-        "query": "\"support vector machine\" in:description"
-      },
-      {
-        "id": "general.topic-artificial-intelligence",
-        "query": "topic:artificial-intelligence"
-      },
-      {
-        "id": "general.topic-deep-learning",
-        "query": "topic:deep-learning"
-      },
-      {
-        "id": "general.topic-machine-learning",
-        "query": "topic:machine-learning"
-      },
-      {
-        "id": "general.transfer-learning",
-        "query": "\"transfer learning\" in:description,readme"
-      },
-      {
-        "id": "general.weak-supervision",
-        "query": "\"weak supervision\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "geo.building-footprints",
-        "query": "\"building footprint extraction\" in:description,readme"
-      },
-      {
-        "id": "geo.change-detection",
-        "query": "\"remote sensing change detection\" in:description,readme"
-      },
-      {
-        "id": "geo.climate-mapping",
-        "query": "\"climate downscaling\" in:readme"
-      },
-      {
-        "id": "geo.crop-monitoring",
-        "query": "\"crop yield prediction\" in:description,readme"
-      },
-      {
-        "id": "geo.earth-observation-models",
-        "query": "\"Earth observation model\" in:description"
-      },
-      {
-        "id": "geo.environmental-monitoring",
-        "query": "\"environmental monitoring\" in:readme"
-      },
-      {
-        "id": "geo.flood-mapping",
-        "query": "\"flood mapping\" in:readme"
-      },
-      {
-        "id": "geo.foundation-models-earth",
-        "query": "\"Earth observation foundation model\" in:description,readme"
-      },
-      {
-        "id": "geo.geospatial-foundation-models",
-        "query": "\"geospatial foundation model\" in:readme"
-      },
-      {
-        "id": "geo.geospatial-knowledge-graph",
-        "query": "\"geospatial knowledge graph\" in:description,readme"
-      },
-      {
-        "id": "geo.hydrology",
-        "query": "\"hydrology machine learning\" in:readme"
-      },
-      {
-        "id": "geo.land-cover",
-        "query": "\"land cover\" in:readme"
-      },
-      {
-        "id": "geo.land-use",
-        "query": "\"land use\" in:readme"
-      },
-      {
-        "id": "geo.lidar",
-        "query": "LiDAR in:description \"deep learning\""
-      },
-      {
-        "id": "geo.mobility-modeling",
-        "query": "\"human mobility\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "geo.object-detection",
-        "query": "\"object detection\" \"remote sensing\" in:description,readme"
-      },
-      {
-        "id": "geo.ocean-remote-sensing",
-        "query": "\"ocean remote sensing\" in:description,readme"
-      },
-      {
-        "id": "geo.precipitation-forecasting",
-        "query": "\"precipitation forecasting\" in:description,readme"
-      },
-      {
-        "id": "geo.sar-imagery",
-        "query": "\"synthetic aperture radar\" in:readme"
-      },
-      {
-        "id": "geo.satellite-imagery",
-        "query": "\"satellite imagery\" in:readme"
-      },
-      {
-        "id": "geo.topic-earth-observation",
-        "query": "topic:earth-observation"
-      },
-      {
-        "id": "geo.topic-geospatial",
-        "query": "topic:geospatial"
-      },
-      {
-        "id": "geo.topic-gis",
-        "query": "topic:gis"
-      },
-      {
-        "id": "geo.topic-remote-sensing",
-        "query": "topic:remote-sensing"
-      },
-      {
-        "id": "geo.trajectory-prediction",
-        "query": "\"trajectory prediction\" geospatial in:description,readme"
-      },
-      {
-        "id": "geo.urban-sensing",
-        "query": "\"urban remote sensing\" in:readme"
-      },
-      {
-        "id": "geo.wildfire-mapping",
-        "query": "\"wildfire detection\" in:description,readme"
-      },
-      {
-        "id": "graph.e3-equivariance",
-        "query": "\"E(3)-equivariant\" in:readme"
-      },
-      {
-        "id": "graph.equivariant-networks",
-        "query": "\"equivariant neural network\" in:description"
-      },
-      {
-        "id": "graph.geometric-learning",
-        "query": "\"geometric deep learning\" in:readme"
-      },
-      {
-        "id": "graph.gnn-description",
-        "query": "\"graph neural network\" in:description"
-      },
-      {
-        "id": "graph.graph-anomaly-detection",
-        "query": "\"graph anomaly detection\" in:description,readme"
-      },
-      {
-        "id": "graph.graph-classification",
-        "query": "\"graph classification\" in:description"
-      },
-      {
-        "id": "graph.graph-contrastive-learning",
-        "query": "\"graph contrastive learning\" in:description"
-      },
-      {
-        "id": "graph.graph-diffusion",
-        "query": "\"graph diffusion model\" in:description"
-      },
-      {
-        "id": "graph.graph-foundation-model",
-        "query": "\"graph foundation model\" in:description"
-      },
-      {
-        "id": "graph.graph-generation",
-        "query": "\"graph generation\" in:readme"
-      },
-      {
-        "id": "graph.graph-transformer",
-        "query": "\"graph transformer\" in:description"
-      },
-      {
-        "id": "graph.hypergraph-neural-network",
-        "query": "\"hypergraph neural network\" in:description"
-      },
-      {
-        "id": "graph.knowledge-graph-embedding",
-        "query": "\"knowledge graph embedding\" in:description"
-      },
-      {
-        "id": "graph.knowledge-graphs",
-        "query": "topic:knowledge-graph"
-      },
-      {
-        "id": "graph.link-prediction",
-        "query": "\"link prediction\" in:description"
-      },
-      {
-        "id": "graph.manifold-learning",
-        "query": "\"manifold learning\" in:description"
-      },
-      {
-        "id": "graph.mesh-graph-learning",
-        "query": "\"mesh graph neural network\" in:description"
-      },
-      {
-        "id": "graph.molecular-graphs",
-        "query": "\"molecular graph neural network\" in:readme"
-      },
-      {
-        "id": "graph.neural-ode-graph",
-        "query": "\"graph neural ODE\" in:description"
-      },
-      {
-        "id": "graph.persistent-homology",
-        "query": "\"persistent homology\" in:description"
-      },
-      {
-        "id": "graph.point-cloud-learning",
-        "query": "\"point cloud deep learning\" in:description"
-      },
-      {
-        "id": "graph.point-clouds",
-        "query": "topic:point-cloud"
-      },
-      {
-        "id": "graph.representation-learning",
-        "query": "\"graph representation learning\" in:readme"
-      },
-      {
-        "id": "graph.signal-processing",
-        "query": "\"graph signal processing\" in:description,readme"
-      },
-      {
-        "id": "graph.temporal-learning",
-        "query": "\"temporal graph\" in:description,readme"
-      },
-      {
-        "id": "graph.topic-gnn",
-        "query": "topic:graph-neural-network"
-      },
-      {
-        "id": "graph.topological-ml",
-        "query": "\"topological machine learning\" in:readme"
-      },
-      {
-        "id": "llm-code-generation",
-        "query": "\"code generation\" \"language model\" in:readme"
-      },
-      {
-        "id": "llm-reasoning",
-        "query": "\"reasoning model\" in:readme"
-      },
-      {
-        "id": "llm-retrieval-augmented-generation",
-        "query": "\"retrieval-augmented generation\" in:readme"
-      },
-      {
-        "id": "llm-text-to-speech",
-        "query": "\"text-to-speech\" \"language model\" in:readme"
-      },
-      {
-        "id": "llm.agents",
-        "query": "topic:ai-agents"
-      },
-      {
-        "id": "llm.ai-agent",
-        "query": "\"AI agent\" in:description"
-      },
-      {
-        "id": "llm.alignment",
-        "query": "\"alignment\" in:description"
-      },
-      {
-        "id": "llm.diffusion",
-        "query": "topic:diffusion-model"
-      },
-      {
-        "id": "llm.diffusion-phrase",
-        "query": "\"diffusion model\" in:description"
-      },
-      {
-        "id": "llm.evaluation",
-        "query": "\"LLM evaluation\" in:readme"
-      },
-      {
-        "id": "llm.finetuning",
-        "query": "\"fine-tuning\" in:description"
-      },
-      {
-        "id": "llm.foundation-models",
-        "query": "\"foundation model\" in:description"
-      },
-      {
-        "id": "llm.generative-ai",
-        "query": "\"generative AI\" in:description"
-      },
-      {
-        "id": "llm.instruction-tuning",
-        "query": "\"instruction tuning\" in:readme"
-      },
-      {
-        "id": "llm.language-model",
-        "query": "\"language model\" in:description"
-      },
-      {
-        "id": "llm.language-models",
-        "query": "topic:large-language-models"
-      },
-      {
-        "id": "llm.large-language-model",
-        "query": "\"large language model\" in:description"
-      },
-      {
-        "id": "llm.long-context",
-        "query": "\"long context\" \"language model\" in:readme"
-      },
-      {
-        "id": "llm.multimodal",
-        "query": "\"multimodal language model\" in:description"
-      },
-      {
-        "id": "llm.parameter-efficient-finetuning",
-        "query": "\"parameter-efficient fine-tuning\" in:readme"
-      },
-      {
-        "id": "llm.preference-optimization",
-        "query": "\"direct preference optimization\" in:readme"
-      },
-      {
-        "id": "llm.quantization",
-        "query": "\"LLM quantization\" in:readme"
-      },
-      {
-        "id": "llm.speculative-decoding",
-        "query": "\"speculative decoding\" in:readme"
-      },
-      {
-        "id": "llm.synthetic-data",
-        "query": "\"synthetic data\" in:description"
-      },
-      {
-        "id": "llm.synthetic-data-generation",
-        "query": "\"synthetic data generation\" in:description"
-      },
-      {
-        "id": "llm.test-time-compute",
-        "query": "\"test-time compute\" \"language model\" in:readme"
-      },
-      {
-        "id": "llm.transformers",
-        "query": "\"transformer\" in:description"
-      },
-      {
-        "id": "materials-battery",
-        "query": "\"battery materials\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "materials-crystal-generation",
-        "query": "\"crystal structure generation\" in:description,readme"
-      },
-      {
-        "id": "materials-crystal-graph",
-        "query": "\"crystal graph\" in:description,readme"
-      },
-      {
-        "id": "materials-dft-prediction",
-        "query": "\"density functional theory\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "materials-informatics",
-        "query": "\"materials informatics\" in:description,readme"
-      },
-      {
-        "id": "materials-perovskite",
-        "query": "perovskite in:description \"machine learning\""
-      },
-      {
-        "id": "materials-polymer-informatics",
-        "query": "polymer informatics in:description,readme"
-      },
-      {
-        "id": "medical.clinical-decision-support",
-        "query": "\"clinical decision support\" in:readme"
-      },
-      {
-        "id": "medical.clinical-nlp",
-        "query": "\"clinical natural language processing\" in:readme"
-      },
-      {
-        "id": "medical.clinical-time-series",
-        "query": "\"clinical time series\" in:description"
-      },
-      {
-        "id": "medical.clinical-trials",
-        "query": "\"clinical trial prediction\" in:description"
-      },
-      {
-        "id": "medical.dermatology",
-        "query": "dermatology AI dermoscopy in:description,readme"
-      },
-      {
-        "id": "medical.diagnosis-prediction",
-        "query": "\"diagnosis prediction\" in:description"
-      },
-      {
-        "id": "medical.drug-response",
-        "query": "\"drug response prediction\" in:description,readme"
-      },
-      {
-        "id": "medical.ecg",
-        "query": "ECG \"deep learning\" in:description,readme"
-      },
-      {
-        "id": "medical.eeg",
-        "query": "EEG \"deep learning\" in:description,readme"
-      },
-      {
-        "id": "medical.ehr",
-        "query": "\"electronic health record\" in:readme"
-      },
-      {
-        "id": "medical.genomics",
-        "query": "\"clinical genomics\" in:description,readme"
-      },
-      {
-        "id": "medical.histopathology",
-        "query": "\"histopathology image analysis\" in:readme"
-      },
-      {
-        "id": "medical.medical-image-reconstruction",
-        "query": "\"MRI reconstruction\" in:description,readme"
-      },
-      {
-        "id": "medical.medical-image-registration",
-        "query": "\"medical image registration\" in:description,readme"
-      },
-      {
-        "id": "medical.medical-image-segmentation",
-        "query": "\"medical image segmentation\" in:description"
-      },
-      {
-        "id": "medical.medical-imaging",
-        "query": "\"medical imaging\" in:readme"
-      },
-      {
-        "id": "medical.medical-language-model",
-        "query": "\"medical language model\" in:description"
-      },
-      {
-        "id": "medical.medical-robotics",
-        "query": "\"surgical robotics\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "medical.neural-decoding",
-        "query": "\"neural data decoding\" in:description"
-      },
-      {
-        "id": "medical.neuroimaging",
-        "query": "\"neuroimaging machine learning\" in:readme"
-      },
-      {
-        "id": "medical.ophthalmology",
-        "query": "ophthalmology AI retinal in:description,readme"
-      },
-      {
-        "id": "medical.pathology",
-        "query": "\"computational pathology\" in:description"
-      },
-      {
-        "id": "medical.pathology-foundation-model",
-        "query": "\"pathology foundation model\" in:description,readme"
-      },
-      {
-        "id": "medical.patient-risk",
-        "query": "\"patient deterioration prediction\" in:description,readme"
-      },
-      {
-        "id": "medical.radiology",
-        "query": "\"radiology machine learning\" in:readme"
-      },
-      {
-        "id": "medical.survival-analysis",
-        "query": "clinical survival prediction in:description,readme"
-      },
-      {
-        "id": "medical.wearable-health",
-        "query": "\"wearable health monitoring\" in:readme"
-      },
-      {
-        "id": "multimodal.3d-generation",
-        "query": "\"3D generation\" in:readme"
-      },
-      {
-        "id": "multimodal.3d-human-pose",
-        "query": "\"3D human pose estimation\" in:readme"
-      },
-      {
-        "id": "multimodal.3d-object-detection",
-        "query": "\"3D object detection\" in:readme"
-      },
-      {
-        "id": "multimodal.3d-reconstruction",
-        "query": "\"3D reconstruction\" in:description"
-      },
-      {
-        "id": "multimodal.audio-visual-learning",
-        "query": "\"audio visual learning\" in:readme"
-      },
-      {
-        "id": "multimodal.embodied-world-models",
-        "query": "\"embodied world model\" in:description"
-      },
-      {
-        "id": "multimodal.gaussian-splatting",
-        "query": "\"Gaussian splatting\" in:description"
-      },
-      {
-        "id": "multimodal.image-text",
-        "query": "\"image-text\" in:description"
-      },
-      {
-        "id": "multimodal.image-to-3d",
-        "query": "\"image to 3D\" in:readme"
-      },
-      {
-        "id": "multimodal.image-to-video",
-        "query": "\"image to video\" in:readme"
-      },
-      {
-        "id": "multimodal.motion-generation",
-        "query": "\"human motion generation\" in:readme"
-      },
-      {
-        "id": "multimodal.nerf",
-        "query": "topic:nerf"
-      },
-      {
-        "id": "multimodal.neural-rendering",
-        "query": "\"neural rendering\" \"radiance fields\" in:description"
-      },
-      {
-        "id": "multimodal.point-cloud",
-        "query": "\"point cloud\" in:description"
-      },
-      {
-        "id": "multimodal.scene-understanding",
-        "query": "\"scene understanding\" in:description"
-      },
-      {
-        "id": "multimodal.text-to-3d",
-        "query": "\"text to 3D\" in:readme"
-      },
-      {
-        "id": "multimodal.text-to-image",
-        "query": "\"text-to-image\" in:description"
-      },
-      {
-        "id": "multimodal.text-to-video",
-        "query": "\"text-to-video\" in:description"
-      },
-      {
-        "id": "multimodal.video-language-model",
-        "query": "\"video language model\" in:readme"
-      },
-      {
-        "id": "multimodal.video-question-answering",
-        "query": "\"video question answering\" in:readme"
-      },
-      {
-        "id": "multimodal.video-understanding",
-        "query": "\"video-language\" in:description"
-      },
-      {
-        "id": "multimodal.vision-language",
-        "query": "\"vision-language\" in:description"
-      },
-      {
-        "id": "multimodal.visual-question-answering",
-        "query": "\"visual question answering\" in:description"
-      },
-      {
-        "id": "multimodal.vlm",
-        "query": "\"vision-language model\" in:description"
-      },
-      {
-        "id": "multimodal.world-models",
-        "query": "\"world model\" in:description"
-      },
-      {
-        "id": "nlp-bm25-retrieval",
-        "query": "BM25 retrieval in:readme"
-      },
-      {
-        "id": "nlp-document-retrieval",
-        "query": "\"document retrieval\" in:readme"
-      },
-      {
-        "id": "nlp-entity-linking",
-        "query": "\"entity linking\" in:readme"
-      },
-      {
-        "id": "nlp-natural-language-inference",
-        "query": "\"natural language inference\" in:readme"
-      },
-      {
-        "id": "nlp-query-understanding",
-        "query": "\"query understanding\" in:readme"
-      },
-      {
-        "id": "nlp-sparse-retrieval",
-        "query": "\"sparse retrieval\" in:readme"
-      },
-      {
-        "id": "nlp-speech-language-modeling",
-        "query": "\"speech language model\" in:readme"
-      },
-      {
-        "id": "nlp.coreference-resolution",
-        "query": "\"coreference resolution\" in:readme"
-      },
-      {
-        "id": "nlp.cross-encoder-reranking",
-        "query": "\"cross-encoder\" \"reranking\" in:readme"
-      },
-      {
-        "id": "nlp.dense-retrieval",
-        "query": "\"dense retrieval\" in:readme"
-      },
-      {
-        "id": "nlp.dependency-parsing",
-        "query": "\"dependency parsing\" in:readme"
-      },
-      {
-        "id": "nlp.dialogue-systems",
-        "query": "\"dialogue system\" in:readme"
-      },
-      {
-        "id": "nlp.information-extraction",
-        "query": "\"information extraction\" in:description"
-      },
-      {
-        "id": "nlp.language-modeling",
-        "query": "\"language modeling\" in:description"
-      },
-      {
-        "id": "nlp.machine-translation",
-        "query": "\"machine translation\" in:description"
-      },
-      {
-        "id": "nlp.named-entity-recognition",
-        "query": "\"named entity recognition\" in:readme"
-      },
-      {
-        "id": "nlp.neural-reranking",
-        "query": "\"neural reranking\" in:description"
-      },
-      {
-        "id": "nlp.question-answering",
-        "query": "\"question answering\" in:description"
-      },
-      {
-        "id": "nlp.rag",
-        "query": "\"retrieval augmented generation\" in:description"
-      },
-      {
-        "id": "nlp.semantic-search",
-        "query": "\"semantic search\" in:description"
-      },
-      {
-        "id": "nlp.sentence-embeddings",
-        "query": "\"sentence embeddings\" in:description"
-      },
-      {
-        "id": "nlp.subword-tokenization",
-        "query": "\"subword tokenization\" in:readme"
-      },
-      {
-        "id": "nlp.text-classification",
-        "query": "\"text classification\" in:description"
-      },
-      {
-        "id": "nlp.text-generation",
-        "query": "\"text generation\" in:readme"
-      },
-      {
-        "id": "nlp.text-summarization",
-        "query": "\"text summarization\" in:description"
-      },
-      {
-        "id": "nlp.topic-modeling",
-        "query": "\"topic modeling\" in:readme"
-      },
-      {
-        "id": "recall.archaeological-remote-sensing",
-        "query": "archaeology \"remote sensing\" in:name,description,readme"
-      },
-      {
-        "id": "recall.case-outcome-prediction",
-        "query": "\"case outcome prediction\" in:name,description,readme"
-      },
-      {
-        "id": "recall.computational-linguistics",
-        "query": "\"computational linguistics\" model in:name,description,readme"
-      },
-      {
-        "id": "recall.computational-social-science",
-        "query": "\"computational social science\" in:name,description,readme"
-      },
-      {
-        "id": "recall.digital-humanities-nlp",
-        "query": "\"digital humanities\" NLP in:name,description,readme"
-      },
-      {
-        "id": "recall.disease-surveillance",
-        "query": "\"disease surveillance\" machine learning in:name,description,readme"
-      },
-      {
-        "id": "recall.educational-recommendation",
-        "query": "\"educational recommendation\" in:name,description,readme"
-      },
-      {
-        "id": "recall.epidemic-forecasting",
-        "query": "\"epidemic forecasting\" in:name,description,readme"
-      },
-      {
-        "id": "recall.epidemiological-modeling",
-        "query": "\"epidemiological modeling\" machine learning in:name,description,readme"
-      },
-      {
-        "id": "recall.grid-stability-prediction",
-        "query": "\"grid stability prediction\" in:name,description,readme"
-      },
-      {
-        "id": "recall.historical-document-recognition",
-        "query": "\"historical document recognition\" in:name,description,readme"
-      },
-      {
-        "id": "recall.infrastructure-damage-detection",
-        "query": "\"infrastructure damage detection\" deep learning in:name,description,readme"
-      },
-      {
-        "id": "recall.insurance-risk-modeling",
-        "query": "\"insurance risk\" machine learning in:name,description,readme"
-      },
-      {
-        "id": "recall.legal-document-classification",
-        "query": "\"legal document classification\" in:name,description,readme"
-      },
-      {
-        "id": "recall.legal-nlp",
-        "query": "legal NLP in:name,description,readme"
-      },
-      {
-        "id": "recall.name.artificial-intelligence",
-        "query": "\"artificial intelligence\" in:name,description,readme"
-      },
-      {
-        "id": "recall.name.deep-learning",
-        "query": "\"deep learning\" in:name,description,readme"
-      },
-      {
-        "id": "recall.name.diffusion-model",
-        "query": "diffusion model in:name,description,readme"
-      },
-      {
-        "id": "recall.name.foundation-model",
-        "query": "foundation model in:name,description,readme"
-      },
-      {
-        "id": "recall.name.language-model",
-        "query": "\"language model\" in:name,description,readme"
-      },
-      {
-        "id": "recall.name.large-language-model",
-        "query": "\"large language model\" in:name,description,readme"
-      },
-      {
-        "id": "recall.name.machine-learning",
-        "query": "\"machine learning\" in:name,description,readme"
-      },
-      {
-        "id": "recall.name.multimodal-model",
-        "query": "multimodal model in:name,description,readme"
-      },
-      {
-        "id": "recall.name.neural-network",
-        "query": "\"neural network\" in:name,description,readme"
-      },
-      {
-        "id": "recall.name.transformer-model",
-        "query": "transformer model in:name,description,readme"
-      },
-      {
-        "id": "recall.player-tracking",
-        "query": "\"player tracking\" computer vision in:name,description,readme"
-      },
-      {
-        "id": "recall.population-health-prediction",
-        "query": "\"population health prediction\" in:name,description,readme"
-      },
-      {
-        "id": "recall.power-system-forecasting",
-        "query": "\"power system forecasting\" in:name,description,readme"
-      },
-      {
-        "id": "recall.social-media-analysis",
-        "query": "\"social media analysis\" NLP in:name,description,readme"
-      },
-      {
-        "id": "recall.sports-analytics",
-        "query": "\"sports analytics\" model in:name,description,readme"
-      },
-      {
-        "id": "recall.structural-health-monitoring",
-        "query": "\"structural health monitoring\" machine learning in:name,description,readme"
-      },
-      {
-        "id": "recall.student-performance-prediction",
-        "query": "\"student performance prediction\" in:name,description,readme"
-      },
-      {
-        "id": "recall26.audio-language-model",
-        "query": "\"audio language model\" in:description,readme"
-      },
-      {
-        "id": "recall26.class-imbalance",
-        "query": "\"class imbalance\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "recall26.image-dehazing",
-        "query": "\"image dehazing\" in:description,readme"
-      },
-      {
-        "id": "recall26.image-denoising",
-        "query": "\"image denoising\" in:description,readme"
-      },
-      {
-        "id": "recall26.long-tailed-recognition",
-        "query": "\"long-tailed recognition\" in:description,readme"
-      },
-      {
-        "id": "recall26.low-light-enhancement",
-        "query": "\"low-light image enhancement\" in:description,readme"
-      },
-      {
-        "id": "recall26.sign-language",
-        "query": "\"sign language\" in:description,readme"
-      },
-      {
-        "id": "recall26.visual-grounding",
-        "query": "\"visual grounding\" in:description,readme"
-      },
-      {
-        "id": "rl.bandits",
-        "query": "\"multi-armed bandit\" in:description"
-      },
-      {
-        "id": "rl.continual",
-        "query": "\"continual reinforcement learning\" in:description,readme"
-      },
-      {
-        "id": "rl.core",
-        "query": "topic:reinforcement-learning"
-      },
-      {
-        "id": "rl.deep",
-        "query": "\"deep reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.deep-q-learning",
-        "query": "\"deep q-learning\" in:description"
-      },
-      {
-        "id": "rl.distributional",
-        "query": "\"distributional reinforcement learning\" in:description,readme"
-      },
-      {
-        "id": "rl.exploration",
-        "query": "\"reinforcement learning exploration\" in:description"
-      },
-      {
-        "id": "rl.games",
-        "query": "topic:game-ai"
-      },
-      {
-        "id": "rl.hierarchical",
-        "query": "\"hierarchical reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.imitation-learning",
-        "query": "\"imitation learning\" in:description"
-      },
-      {
-        "id": "rl.inverse",
-        "query": "\"inverse reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.model-based",
-        "query": "\"model-based reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.model-free",
-        "query": "\"model-free reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.multiagent",
-        "query": "\"multi-agent reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.offline",
-        "query": "\"offline reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.planning",
-        "query": "\"reinforcement learning planning\" in:description"
-      },
-      {
-        "id": "rl.policy-gradient",
-        "query": "\"policy gradient\" in:description"
-      },
-      {
-        "id": "rl.ppo",
-        "query": "\"proximal policy optimization\" in:description"
-      },
-      {
-        "id": "rl.preference",
-        "query": "\"preference-based reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.reinforcement-learning-from-human-feedback",
-        "query": "\"reinforcement learning from human feedback\" in:description"
-      },
-      {
-        "id": "rl.safe",
-        "query": "\"safe reinforcement learning\" in:description"
-      },
-      {
-        "id": "rl.soft-actor-critic",
-        "query": "\"soft actor-critic\" in:description"
-      },
-      {
-        "id": "robotics.autonomous-driving",
-        "query": "\"autonomous driving\" in:description"
-      },
-      {
-        "id": "robotics.behavior-cloning",
-        "query": "\"behavior cloning\" in:description"
-      },
-      {
-        "id": "robotics.diffusion-policy",
-        "query": "\"diffusion policy\" in:description"
-      },
-      {
-        "id": "robotics.domain-randomization",
-        "query": "\"domain randomization\" in:description"
-      },
-      {
-        "id": "robotics.embodied-ai",
-        "query": "\"embodied AI\" in:description"
-      },
-      {
-        "id": "robotics.grasping",
-        "query": "\"robot grasping\" in:description"
-      },
-      {
-        "id": "robotics.imitation-learning",
-        "query": "\"imitation learning\" robotics in:description"
-      },
-      {
-        "id": "robotics.locomotion",
-        "query": "\"robot locomotion\" in:description"
-      },
-      {
-        "id": "robotics.manipulation",
-        "query": "\"robot manipulation\" in:description"
-      },
-      {
-        "id": "robotics.navigation",
-        "query": "\"robot navigation\" in:description"
-      },
-      {
-        "id": "robotics.robot-control",
-        "query": "\"robot control\" in:description"
-      },
-      {
-        "id": "robotics.robot-foundation-model",
-        "query": "robot foundation model in:description,readme"
-      },
-      {
-        "id": "robotics.robot-learning",
-        "query": "\"robot learning\" in:description"
-      },
-      {
-        "id": "robotics.robot-manipulation-learning",
-        "query": "\"robot manipulation learning\" in:readme"
-      },
-      {
-        "id": "robotics.robot-motion-planning",
-        "query": "\"robot motion planning\" in:description"
-      },
-      {
-        "id": "robotics.robotics-reinforcement-learning",
-        "query": "\"robotics reinforcement learning\" in:description"
-      },
-      {
-        "id": "robotics.robotics-topic",
-        "query": "topic:robotics"
-      },
-      {
-        "id": "robotics.sim2real",
-        "query": "\"sim-to-real\" in:description"
-      },
-      {
-        "id": "robotics.slam",
-        "query": "SLAM robotics in:description"
-      },
-      {
-        "id": "robotics.tactile-learning",
-        "query": "tactile robot learning in:description,readme"
-      },
-      {
-        "id": "robotics.teleoperation",
-        "query": "robot teleoperation learning in:description,readme"
-      },
-      {
-        "id": "robotics.visual-language-action",
-        "query": "\"vision-language-action\" in:description"
-      },
-      {
-        "id": "robotics.visuomotor-policy",
-        "query": "\"visuomotor policy\" in:description"
-      },
-      {
-        "id": "science.astronomy",
-        "query": "astronomy in:readme"
-      },
-      {
-        "id": "science.astrophysics",
-        "query": "astrophysics in:description"
-      },
-      {
-        "id": "science.climate-modeling",
-        "query": "\"climate modeling\" in:readme"
-      },
-      {
-        "id": "science.computational-mechanics",
-        "query": "\"computational mechanics\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "science.cosmology",
-        "query": "cosmology in:description"
-      },
-      {
-        "id": "science.differentiable-simulation",
-        "query": "\"differentiable simulation\" in:readme"
-      },
-      {
-        "id": "science.exoplanets",
-        "query": "exoplanet in:description \"machine learning\""
-      },
-      {
-        "id": "science.fluid-dynamics",
-        "query": "\"fluid dynamics\" in:description"
-      },
-      {
-        "id": "science.fusion-plasma",
-        "query": "fusion plasma in:description \"machine learning\""
-      },
-      {
-        "id": "science.gravitational-waves",
-        "query": "\"gravitational wave\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "science.inverse-problems",
-        "query": "\"inverse problems\" in:description"
-      },
-      {
-        "id": "science.neural-operators",
-        "query": "\"neural operator\" in:description"
-      },
-      {
-        "id": "science.ocean-modeling",
-        "query": "\"ocean modeling\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "science.particle-physics",
-        "query": "\"particle physics\" in:description"
-      },
-      {
-        "id": "science.photonics",
-        "query": "photonics \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "science.physics",
-        "query": "physics in:description"
-      },
-      {
-        "id": "science.physics-informed-neural-networks",
-        "query": "\"physics-informed neural network\" in:description"
-      },
-      {
-        "id": "science.plasma-physics",
-        "query": "\"plasma physics\" in:description"
-      },
-      {
-        "id": "science.quantum-materials",
-        "query": "\"quantum materials\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "science.quantum-physics",
-        "query": "\"quantum many-body\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "science.remote-sensing",
-        "query": "\"remote sensing\" in:description"
-      },
-      {
-        "id": "science.scientific-machine-learning",
-        "query": "\"scientific machine learning\" in:readme"
-      },
-      {
-        "id": "science.seismic",
-        "query": "\"seismic data\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "science.turbulence",
-        "query": "turbulence in:description \"machine learning\""
-      },
-      {
-        "id": "science.weather-forecasting",
-        "query": "\"weather forecasting\" climate in:description"
-      },
-      {
-        "id": "science.weather-nowcasting",
-        "query": "nowcasting in:description \"machine learning\""
-      },
-      {
-        "id": "specialized.carbon-footprint-training",
-        "query": "\"carbon footprint\" \"deep learning\" training in:description,readme"
-      },
-      {
-        "id": "specialized.computational-pathology-ml",
-        "query": "\"computational pathology\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "specialized.coreset-selection",
-        "query": "\"coreset selection\" in:description,readme"
-      },
-      {
-        "id": "specialized.curriculum-learning",
-        "query": "\"curriculum learning\" in:description,readme"
-      },
-      {
-        "id": "specialized.data-centric-ai",
-        "query": "\"data-centric AI\" in:description,readme"
-      },
-      {
-        "id": "specialized.data-shapley",
-        "query": "\"data Shapley\" in:description,readme"
-      },
-      {
-        "id": "specialized.dataset-distillation",
-        "query": "\"dataset distillation\" in:description,readme"
-      },
-      {
-        "id": "specialized.event-camera-deep-learning",
-        "query": "\"event camera\" \"deep learning\" in:description,readme"
-      },
-      {
-        "id": "specialized.fmri-decoding-ml",
-        "query": "\"fMRI decoding\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "specialized.machine-unlearning",
-        "query": "\"machine unlearning\" in:description,readme"
-      },
-      {
-        "id": "specialized.mechanistic-interpretability",
-        "query": "\"mechanistic interpretability\" in:description,readme"
-      },
-      {
-        "id": "specialized.medical-image-registration-deep-learning",
-        "query": "\"medical image registration\" \"deep learning\" in:description,readme"
-      },
-      {
-        "id": "specialized.pathology-foundation-model",
-        "query": "\"pathology foundation model\" \"machine learning\" in:description,readme"
-      },
-      {
-        "id": "specialized.spiking-neural-network",
-        "query": "\"spiking neural network\" in:description,readme"
-      },
-      {
-        "id": "specialized.test-time-adaptation",
-        "query": "\"test-time adaptation\" in:description,readme"
-      },
-      {
-        "id": "timeseries.anomaly-detection",
-        "query": "\"time series anomaly detection\" in:description"
-      },
-      {
-        "id": "timeseries.classification",
-        "query": "\"time series classification\" in:description"
-      },
-      {
-        "id": "timeseries.clustering",
-        "query": "\"time series clustering\" in:description,readme"
-      },
-      {
-        "id": "timeseries.energy-forecasting",
-        "query": "\"energy forecasting\" in:description"
-      },
-      {
-        "id": "timeseries.event-forecasting",
-        "query": "\"event forecasting\" in:description"
-      },
-      {
-        "id": "timeseries.forecasting",
-        "query": "\"time series forecasting\" in:description"
-      },
-      {
-        "id": "timeseries.foundation-models",
-        "query": "\"time series foundation model\" in:description"
-      },
-      {
-        "id": "timeseries.imputation",
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