Add verified remote artifact checks, full experiment timings and consolidated control results

FINAL_REPORT.md

@@ -175,8 +175,57 @@ These are diagnostic fragments, not verified physical field instances. Only fixe
 
 [Generalization audit](ablations/10_plateau_breakthrough/workspace/class_query_sits_research_20261010/generalization_audit.json). These diagnostic training scores were measured on the saved best-validation checkpoints without augmentation.
 
+
+### ConvNeXt-inspired controls and incumbent-building round
+
+These controls used the same supervised sampling. They are small custom scratch networks, not official externally pretrained ConvNeXt or ConvNeXt V2 backbones.
+
+# ConvNeXt experiment results
+
+All eight 100-epoch runs and the explicitly requested full fold-4 test sweep are complete. Training used the same fixed-32 sample protocol and seed 3407. ConvNeXt candidates were trained from scratch as auxiliary predictors; they did not fine-tune or replace the original pretrained checkpoint.
+
+## Validation and test results
+
+Baseline recommended system: **52.5639845% validation mIoU / 49.4555473% test mIoU**. The standalone test score is each new model by itself. The selected-blend test score combines it with the incumbent using the weight selected on validation. Test labels did not select or tune weights.
+
+| Experiment | Best checkpoint epoch | Standalone val mIoU | Selected blend val mIoU | Standalone test mIoU | Selected blend test mIoU | Blend Δ vs baseline |
+|---|---:|---:|---:|---:|---:|---:|
+| CNN control | 87 | 46.1393% | 52.5640% | 42.6678% | 49.4555% | 0.0000 pp |
+| Temporal ConvNeXt V1, k=3 | 63 | 34.3460% | 52.5640% | 32.4532% | 49.4555% | 0.0000 pp |
+| Temporal ConvNeXt V1, k=7 | 75 | 33.8808% | 52.5640% | 30.6907% | 49.4555% | 0.0000 pp |
+| Temporal ConvNeXt V2, k=7 | 73 | 40.3216% | 52.5640% | 35.3459% | 49.4555% | 0.0000 pp |
+| Temporal ConvNeXt V2, k=15 | 79 | 39.3190% | **52.5953%** | 36.6740% | **49.1282%** | **−0.3273 pp** |
+| Spatial ConvNeXt V1 | 72 | 40.9261% | 52.5640% | 36.6155% | 49.4555% | 0.0000 pp |
+| Spatial ConvNeXt V2 | 78 | 40.4051% | 52.5640% | 34.7727% | 49.4555% | 0.0000 pp |
+| Spatial InceptionNeXt-inspired mixer | 71 | 39.6000% | 52.5640% | 35.7605% | 49.4555% | 0.0000 pp |
+
+All mIoU values are for 18 foreground classes. For seven candidates validation selected blend weight 0, so the selected blend is exactly the established system and naturally has its baseline test score. Temporal ConvNeXt V2 k=15 was the only candidate with a nonzero selected weight (0.1); its small **+0.0313 validation-point** change did not transfer to test and reduced test mIoU by **0.3273 points**. None of the ConvNeXt candidates improved the incumbent.
+
+For the k=15 blend, test overall accuracy was **81.0092%**, macro F1 was **61.9696%**, and kappa was **0.7629**. The incumbent baseline was **81.0927% OA**, **62.3414% macro F1**, and **0.7643 kappa**. Standalone candidate OA and macro-F1 are in each `test_results.json` beside its confusion matrix and per-class IoU.
+
+
+
+# Score-improvement round: completed results
+
+**The combined model achieves 53.3274080% validation mIoU and 50.2463326% Test mIoU**, exceeding the 50% target. Both queues completed successfully on 2026-10-10 at 16:42:43 UTC. Total elapsed time was approximately 47 minutes, including training, validation selection and test evaluation. No jobs remain in this round.
+
+| Configuration | Validation mIoU | Test mIoU | Test change vs reference | Decision |
+|---|---:|---:|---:|---|
+| Comparison reference | 52.5639845% | 49.4555473% | — | Retained for comparison |
+| Reference + Gaussian class-distribution readout | 53.1273049% | 49.6419293% | +0.1863819 pp | Useful addition |
+| Reference + structured-SSL checkpoint | 53.1051538% | 50.0620847% | +0.6065374 pp | Useful addition |
+| Matched raw-branch CNN, standalone | 45.8280199% | Not evaluated | — | Rejected on validation; selected blend weight zero |
+| Temporal first-difference CNN, standalone | 44.9030032% | Not evaluated | — | Rejected on validation; selected blend weight zero |
+| **Combined Gaussian + structured recipe** | **53.3274080%** | **50.2463326%** | **+0.7907853 pp** | **Best validation and test configuration in this round** |
+
+CNN rows show standalone validation scores; their selected blends both retain the reference's 52.5639845%. Both CNNs completed 100 epochs. Their test-stage jobs successfully applied the validation promotion gate and skipped inference; a completed queue entry does not mean a standalone test score exists.
+
+
+
 ## 7. Timing and resource interpretation
 
+[Recorded durations for every new batch and per-job CSV/JSON](reports/oct11/JOB_TIMINGS.md) distinguish wall time from waiting and GPU compute.
+
 The recorded final transfer queue took **300.34134 seconds (5.0057 minutes)**. This includes the recorded cache/fit/promotion/test jobs but uses already generated incumbent probability arrays. It excludes download, research implementation, independent audit and the training/inference that originally produced incumbent caches. Do not advertise five minutes as end-to-end model development or uncached full-ensemble cost. Per-job durations remain in each batch's `status.json` / `completion.json`; epoch histories and logs are uploaded with the corresponding experiment.
 
 The 512-pixel GPU smoke test took approximately0.277seconds with peak allocated0.252GB; it is not a full-patch or all-ensemble memory benchmark. New encoder fine-tuning epochs: **zero**. Training of prior CNN/query controls is separately recorded (100 or150epochs depending on the batch).
@@ -187,6 +236,8 @@ The original completion audit independently rebuilt confusion matrices from save
 
 The portable publication code additionally verifies encoder/readout SHA-256 values and exactly replays the complete score from the released arrays. Fresh optical inference is distinct from cached ensemble replay; [commands and boundaries](main_method/README.md#reproduction) document both. Test exploration throughout the project's history is acknowledged rather than hidden.
 
+[Remote publication verification](reports/oct11/REMOTE_PUBLICATION_VERIFICATION.json) confirms the initial upload: 665 staged files match remote hashes, 41 protected pretrained entries are unchanged, and all previous remote paths remain. The final documentation addendum adds timings and this verification record.
+
 ## 9. Repository, checkpoints, figures and reproduction
 
 - [Main method](main_method/README.md), [main code](main_method/code/), [current weights](main_method/checkpoints/32X32_tessera_transfer_seed3407/), [configuration](main_method/configurations/32X32_tessera_transfer_seed3407/).

docs/OCT11_RELEASE_REPORT.md

@@ -2,7 +2,7 @@
 
 ## Result and scope
 
-Published result: **64.1505% validation / 58.6346% Test mIoU**, **+8.3883 test points** over50.2463%. Pixel accuracy84.8792%, macro F170.9305%, kappa0.814272. The final transfer queue completed in5.01minutes using cached incumbent predictions; this excludes download, implementation, audit and historical component training.
+Published result: **64.1505% validation / 58.6346% Test mIoU**, **+8.3883 test points** over 50.2463%. Pixel accuracy 84.8792%, macro F1 70.9305%, kappa 0.814272. The final transfer queue completed in 5.01 minutes using cached incumbent predictions; this excludes download, implementation, audit and historical component training.
 
 **Comparison scope:** supervised sample lists, duplicates, folds, crop coordinates and local numerical seed match the downloaded STCLN code. This is an **external-pretraining + acquisition-calendar track**, not an identical-pretraining-budget STCLN comparison. External pretraining geographic disjointness is not certified. Checkpoint-specific normalization, temporal padding, selection criteria, ensemble size and confusion-matrix conventions also differ. Repeated test inspection makes the project exploratory; validation-frozen promotion in this round does not erase that history.
 
@@ -26,8 +26,8 @@ The release validation record reports actual checks performed on the staged code
 
 - Encoder and fitted-classifier SHA-256 values match the retained winning files.
 - Refit from original training features reproduces every classifier parameter exactly.
-- Fresh raw-data validation extraction reproduces every saved optical probability exactly: 155,648 pixel entries across the 76-entry/two-crop pass, maximum absolute difference0.
-- Complete 482-patch probability replay matches the audited confusion matrix and58.6346223%TestmIoU; every raw test target is checked.
+- Fresh raw-data validation extraction reproduces every saved optical probability exactly: 155,648 pixel entries across the 76-entry/two-crop pass, maximum absolute difference 0.
+- Complete 482-patch probability replay matches the audited confusion matrix and 58.6346223% Test mIoU; every raw test target is checked.
 - Main-method Python files parse successfully; current document links and rendered figures are checked.
 
 [Machine-readable code validation](../reports/oct11/RELEASE_CODE_VALIDATION.json) records these results. Cached replay and fresh optical inference remain explicitly distinguished.
@@ -40,6 +40,14 @@ The original pretrained directory is excluded from the upload overlay. Before/af
 
 Cached replay reproduces the full reported result; fresh optical extraction reproduces the new branch. It does not generate every old ensemble component from scratch. Historical research drivers retain original VM dependency/path assumptions, which are explicitly documented. Five minutes is the final pilot time with cached incumbent predictions, not the full model-development compute budget. Dataset metadata/raw labels and original PASTIS files are not uploaded as model weights.
 
+## Remote verification and timing records
+
+The initial three-commit upload ended at `60afa710af2a695cb4d9a2a6f9a9a8259d824b49`. Remote verification checked **665 files** against local content hashes, confirmed **41 protected pretrained entries unchanged**, and found **all previous remote paths retained**. [Verification JSON](../reports/oct11/REMOTE_PUBLICATION_VERIFICATION.json).
+
+[Batch/per-job timing table](../reports/oct11/JOB_TIMINGS.md) includes the original ConvNeXt queue, incumbent-building jobs and all later goal batches. It distinguishes elapsed wall time from explicit waits and warns about internal waits and overlapping batches. CSV/JSON values are derived from actual recorded timestamps/durations; GPU hours are not inferred.
+
+This documentation addendum adds timing tables, the remote verification record and an inventory excluding local Hugging Face download/upload bookkeeping. No checkpoint content is changed.
+
 ## Publication record
 
 The upload uses the Hugging Face CLI and adds/replaces only staged paths. The remote commit and protected-file verification are reported to the user after upload.

reports/oct11/JOB_TIMINGS.csv

@@ -0,0 +1,88 @@
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reports/oct11/JOB_TIMINGS.md

@@ -0,0 +1,21 @@
+# Recorded experiment timings
+
+These are recorded **wall-clock** durations, not measured GPU compute hours. Batches can overlap, explicit and internal waits are included, and later explicit test sweeps may be recorded separately. Do not sum these as a GPU bill.
+
+| Batch | Recorded elapsed minutes | Explicit wait minutes | Recorded jobs |
+|---|---:|---:|---:|
+| calendar_cnn_research_20261010 | 23.02 | 5.07 | 7 |
+| ceiling_research_20261010 | 72.72 | 0.00 | 20 |
+| class_query_sits_research_20261010 | 17.21 | 0.05 | 8 |
+| convnext_research_20261010 | 140.69 | 0.00 | 17 |
+| covariance_research_20261010 | 1.78 | 0.00 | 6 |
+| query_ssl_research_20261010 | 30.11 | 9.76 | 8 |
+| score_breakthrough_20261010 | 30.46 | 0.00 | 8 |
+| structured_readout_research_20261010 | 65.24 | 0.00 | 6 |
+| tessera_transfer_research_20261010 | 5.01 | 0.00 | 7 |
+
+The structured-readout feature-cache job includes waiting for the shared GPU; its 65-minute wall duration is not 65 minutes of feature computation. The ConvNeXt queue table records the original scheduled jobs; its later explicitly requested all-model test sweep is retained in separate result files.
+
+The foundation-transfer pilot took **5.0057 minutes**, using previously cached incumbent predictions. It excludes download, implementation, independent audit and historical component training. The checkpoint encoder was frozen; classifier fitting has no gradient-training epochs.
+
+[Per-job CSV](JOB_TIMINGS.csv) / [full timing JSON](JOB_TIMINGS.json). Original status/log records remain with each experiment.

reports/oct11/RELEASE_INVENTORY.json

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+}