Polish WildFIRE-FM model card with final paper assets

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.gitattributes +4 -0
README.md +64 -63
artifacts/manifests/paper_outputs.sha256 +6 -3
artifacts/manifests/paper_outputs.yml +70 -60
artifacts/results/fireprone_contract_progression_summary.json +328 -328
artifacts/results/selection_regret_all_backbones_20260504.csv +25 -0
artifacts/results/selection_regret_all_backbones_20260504.json +0 -0
artifacts/results/selection_regret_scope_sweep_20260505.csv +45 -45
artifacts/results/selection_regret_scope_sweep_20260505.json +220 -220
assets/{primary_rank_change_preview.png → overview_final.png} +2 -2
assets/{selection_regret_preview.png → primary_rank_change_final.png} +2 -2
assets/release_contents.svg +33 -0
assets/{task_rank_map_preview.png → selection_regret_final.png} +2 -2
assets/supporting_rank_map_final.png +3 -0
assets/wildfire_fm_model_card.svg +41 -0
docs/huggingface_release_design.md +19 -8
scripts/audit_release.py +6 -4

.gitattributes CHANGED Viewed

@@ -39,3 +39,7 @@ assets/task_rank_map_preview.png filter=lfs diff=lfs merge=lfs -text
 paper/manuscript_final.pdf filter=lfs diff=lfs merge=lfs -text
 paper_outputs/figures/fig_selection_regret_scatter.pdf filter=lfs diff=lfs merge=lfs -text
 paper_outputs/figures/overview_wildfire.pdf filter=lfs diff=lfs merge=lfs -text

 paper/manuscript_final.pdf filter=lfs diff=lfs merge=lfs -text
 paper_outputs/figures/fig_selection_regret_scatter.pdf filter=lfs diff=lfs merge=lfs -text
 paper_outputs/figures/overview_wildfire.pdf filter=lfs diff=lfs merge=lfs -text
+assets/overview_final.png filter=lfs diff=lfs merge=lfs -text
+assets/primary_rank_change_final.png filter=lfs diff=lfs merge=lfs -text
+assets/selection_regret_final.png filter=lfs diff=lfs merge=lfs -text
+assets/supporting_rank_map_final.png filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -15,62 +15,36 @@ pretty_name: WildFIRE-FM
 # WildFIRE-FM
-**WildFIRE-FM** is a wildfire-specialized regional reference backbone for gridded wildfire prediction and fixed-contract evaluation. It is trained on California regional weather, active-fire supervision, static fuel/canopy/exposure layers, and event-level wildfire resources used by supporting tasks in the paper.
-This repository is organized as a model release first, with paper artifacts included for reproducibility. It contains the released WildFIRE-FM checkpoints, model configuration metadata, final manuscript artifacts, and scripts used to rebuild the public paper figures/tables from compact summaries. Raw data are **not** redistributed here.
-## What Is In This Release
-**Model weights.** Five seeded WildFIRE-FM occupancy checkpoints are provided under `models/wildfire_fm/checkpoints/seed_*/best_firms_prauc.pt`. The checkpoints are compact U-Net PyTorch state files trained for 12-hour wildfire occupancy on a 5 km EPSG:5070 grid.
-**Model metadata.** Training configs, run summaries, tile summaries, and checkpoint hashes are under `models/wildfire_fm/`.
-**Evaluation artifacts.** Final paper figures, tables, and compact result summaries are under `paper_outputs/` and `artifacts/results/`.
-**Data-source notes.** The data used by the study are public or provider-hosted, but must be obtained separately. See `data_sources/DATA_SOURCES.md`.
-## Model At A Glance
-| Item | WildFIRE-FM release detail |
 |---|---|
 | Task | 12-hour gridded wildfire occupancy prediction |
-| Region/grid | California regional grid, 5 km, EPSG:5070 |
 | Inputs | 16 channels: weather fields, validity masks, static fuel/canopy/exposure layers |
-| Backbone | Compact U-Net with occupancy and auxiliary spatial-support heads |
 | Training split | June-August 2024 train, September 2024 validation, October 2024 test |
-| Seeds | 1, 7, 42, 99, 123 |
-| Raw data | Not bundled; source list and access notes are documented |
-## Main Evaluation Results
-The paper evaluates WildFIRE-FM and ten Earth-FM baselines under fixed task contracts. Scores below are final-paper mean ± std values.
-| Contract | WildFIRE-FM | Best comparator | Reading |
-|---|---:|---:|---|
-| Occupancy union F1 (%) | 59.0656 ± 2.7372 | 60.1506 ± 7.5865, ClimaX | Matching rule changes the apparent winner. |
-| Fire-spread AP (%) | 30.0900 ± 1.2500 | 16.6221 ± 1.6965, Aurora | WildFIRE-FM leads the spread AP contract. |
-| Final burned-area log-RMSE | 1.1657 ± 0.0126 | 1.3070 ± 0.0980, DLWP | Lower is better; task form matters. |
-| Analog nDCG@10 | 0.5099 ± 0.0336 | 0.5086 ± 0.0440, AlphaEarth | Retrieval behaves differently from spatial decision tasks. |
-| Smoke PM2.5 RMSE | 4.4646 ± 0.0060 | 4.4403 ± 0.0488, AlphaEarth | Supporting tasks do not preserve one universal ranking. |
-| Extreme-heat RMSE-C | 0.2179 ± 0.0043 | 0.2194 ± 0.0039, AlphaEarth | Error metrics use a separate contract. |
-## Visual Summary
-**Head-selection sensitivity under fixed features.** The plot shows selection regret when a head chosen by PR-AUC differs from the head chosen by the decision metric.
-![Selection-regret preview](assets/selection_regret_preview.png)
-**Task-form rank changes.** The supporting-task rank map shows that backbone ordering changes when the task form and metric family change.
-![Task-rank map preview](assets/task_rank_map_preview.png)
-**Primary-task rank changes.** The primary-task rank-change map summarizes how occupancy and spread rankings change across fixed matching rules.
-![Primary-rank-change preview](assets/primary_rank_change_preview.png)
-## Loading A Checkpoint
-The released weights are PyTorch checkpoints. The model architecture used by the checkpoints is documented in `models/wildfire_fm/modeling_unet.py` and mirrors the compact U-Net used in the paper.
 ```python
 import torch
@@ -93,46 +67,73 @@ model.load_state_dict(state)
 model.eval()
 ```
-The raw gridded inputs are not included. To run inference on new data, construct the same 16-channel grid described in `data_sources/DATA_SOURCES.md` and the paper's Section 2.
-## Data Used By The Study
-The study uses public or provider-hosted resources but does not redistribute the raw data:
 - NOAA HRRR fields for regional weather inputs.
 - NASA FIRMS active-fire detections for occupancy supervision.
 - LANDFIRE fuel and canopy layers for static landscape context.
 - Wildfire Risk to Communities housing density and LandScan population for exposure context.
-- WFIGS and MTBS event-level resources for supporting burned-area and analog tasks.
 - External Earth-FM/backbone assets for comparator features.
-See `data_sources/DATA_SOURCES.md` for source roles and access entry points.
-## Repository Layout
-```text
-models/wildfire_fm/        WildFIRE-FM configs, metrics, checkpoint manifest, and model code
-paper/manuscript_final.pdf final paper PDF from the current release bundle
-paper_outputs/             final paper figures and TeX table blocks
-artifacts/results/         compact CSV/JSON summaries used by paper outputs
-experiments/               sanitized rerun scripts and Slurm template
-data_sources/              data-source notes; no raw data included
-scripts/                   paper-output rebuild and audit utilities
-```
 ## Reproducing Released Paper Outputs
-The lightweight paper-output check uses compact released artifacts and does not require raw data or GPUs.
 ```bash
 python3 scripts/reproduce_paper_outputs.py
 ```
-This rebuilds selected figures, checks hashes, and audits the release for stale labels or placeholders. Full raw-data reruns require separately downloaded source data and local feature caches.
 ## Limitations
-This release is not a raw-data mirror and is not a general global wildfire forecasting product. WildFIRE-FM is a regional reference backbone trained for the study's fixed-contract comparisons. Use outside the California regional grid requires new preprocessing, validation, and contract-specific evaluation.
 ## Citation

 # WildFIRE-FM
+![WildFIRE-FM summary](assets/wildfire_fm_model_card.svg)
+**WildFIRE-FM** is a wildfire-specialized regional reference backbone for 12-hour gridded wildfire occupancy prediction on a 5 km California grid. It is released with five seeded PyTorch checkpoints, model code, final-paper artifacts, and data-source notes. The raw data are **not** redistributed.
+The model is intended as a reproducible reference backbone for fixed-contract wildfire evaluation, not as a general global wildfire forecasting product. It was trained with regional weather, active-fire supervision, static fuel/canopy/exposure layers, and event-level wildfire resources used by supporting tasks in the paper.
+## Release Contents
+![Release contents](assets/release_contents.svg)
+**Weights.** Five seeded checkpoints are available at `models/wildfire_fm/checkpoints/seed_*/best_firms_prauc.pt`. Each file is listed with SHA-256 and byte size in `models/wildfire_fm/checkpoint_manifest.json`.
+**Model code.** The compact U-Net definition is provided in `models/wildfire_fm/modeling_unet.py`, with a short loading example below.
+**Paper artifacts.** The final manuscript PDF and the final paper figures/tables are included under `paper/` and `paper_outputs/`. Compact CSV/JSON summaries are under `artifacts/results/`.
+**Data notes.** Data sources and access entry points are documented in `data_sources/DATA_SOURCES.md`; users must obtain source data from the original providers.
+## Model Details
+| Field | Value |
 |---|---|
 | Task | 12-hour gridded wildfire occupancy prediction |
+| Grid | California regional grid, 5 km, EPSG:5070 |
 | Inputs | 16 channels: weather fields, validity masks, static fuel/canopy/exposure layers |
+| Architecture | Compact U-Net with occupancy and auxiliary spatial-support heads |
 | Training split | June-August 2024 train, September 2024 validation, October 2024 test |
+| Released seeds | 1, 7, 42, 99, 123 |
+## Quick Load
 ```python
 import torch
 model.eval()
 ```
+The checkpoint expects the same 16-channel gridded input described in the paper and in `data_sources/DATA_SOURCES.md`. This repository does not include raw HRRR, FIRMS, LANDFIRE, WRC, LandScan, WFIGS, MTBS, or comparator feature caches.
+## Evaluation Snapshot
+The paper evaluates WildFIRE-FM and ten Earth-FM comparators under fixed task contracts. A few final-paper WildFIRE-FM values are:
+- **Occupancy union F1:** `59.0656 ± 2.7372` percent.
+- **Fire-spread AP:** `30.0900 ± 1.2500` percent.
+- **Final burned-area log-RMSE:** `1.1657 ± 0.0126`, where lower is better.
+- **Analog retrieval nDCG@10:** `0.5099 ± 0.0336`.
+- **Smoke PM2.5 RMSE:** `4.4646 ± 0.0060`, where lower is better.
+- **Extreme-heat RMSE-C:** `0.2179 ± 0.0043`, where lower is better.
+The full final-paper tables are included as TeX blocks under `paper_outputs/tables/`.
+### Fixed-Contract Checks From The Final Paper
+**Head-selection regret.** This final-paper figure shows that choosing a lightweight head by a ranking metric can lose decision performance under the same frozen features.
+![Head-selection regret](assets/selection_regret_final.png)
+**Supporting-task rank map.** This final-paper figure shows that model ordering changes across burned area, analog retrieval, smoke PM2.5, and extreme heat task contracts.
+![Supporting task rank map](assets/supporting_rank_map_final.png)
+**Primary-task rank changes.** This final-paper figure summarizes rank changes across fixed primary-task contracts.
+![Primary rank changes](assets/primary_rank_change_final.png)
+## Data Sources
+The study uses public or provider-hosted resources, but the processed data are not bundled here:
 - NOAA HRRR fields for regional weather inputs.
 - NASA FIRMS active-fire detections for occupancy supervision.
 - LANDFIRE fuel and canopy layers for static landscape context.
 - Wildfire Risk to Communities housing density and LandScan population for exposure context.
+- WFIGS and MTBS event-level resources for burned-area and analog tasks.
 - External Earth-FM/backbone assets for comparator features.
+See `data_sources/DATA_SOURCES.md` for source roles and access links.
 ## Reproducing Released Paper Outputs
+The lightweight check verifies the released final-paper artifacts from compact summaries. It does not require raw data or GPUs.
 ```bash
 python3 scripts/reproduce_paper_outputs.py
 ```
+Full raw-data reruns require separately downloaded source data, local feature caches, and cluster-specific paths. Sanitized reference scripts and a Slurm template are provided under `experiments/`.
+## Repository Layout
+```text
+models/wildfire_fm/        model code, manifests, and checkpoint metadata
+paper/                     final manuscript PDF and LaTeX source snapshot
+paper_outputs/             final paper figures and TeX table blocks
+artifacts/results/         compact CSV/JSON summaries for released outputs
+experiments/               sanitized raw-rerun references and Slurm template
+data_sources/              source-data roles and access notes
+scripts/                   artifact verification and figure/table rebuild helpers
+```
 ## Limitations
+WildFIRE-FM is a regional reference model trained for the paper's fixed-contract comparisons. Use outside the California regional grid requires new preprocessing, validation, and contract-specific evaluation. The repository does not provide operational alerts, raw data, or third-party comparator weights.
 ## Citation

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artifacts/manifests/paper_outputs.yml CHANGED Viewed

@@ -1,60 +1,70 @@
-figures:
-  fig:toy_occupancy_contract:
-    output: paper_outputs/figures/matching.pdf
-    type: static_vector
-  fig:task_contract_tiles:
-    output: paper_outputs/figures/fig_task_contract_tiles.pdf
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-    builder: scripts/build_selection_regret_rq2_figure.py
-    inputs:
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-    builder: scripts/build_fireprone_contract_progression_figure.py
-    inputs:
-      - artifacts/results/fireprone_contract_progression_summary.json
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-    builder: scripts/build_task_rank_map.py
-    inputs:
-      - paper_outputs/tables/tab_primary_results.tex
-      - paper_outputs/tables/tab_supporting_results.tex
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-  tab:primary_results:
-    output: paper_outputs/tables/tab_primary_results.tex
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-    output: paper_outputs/tables/tab_supporting_results.tex
-  tab:app_matching_rule_params:
-    output: paper_outputs/tables/tab_app_matching_rule_params.tex
-  tab:app_contract_params_full:
-    output: paper_outputs/tables/tab_app_contract_params_full.tex
-  tab:app_scope_params:
-    output: paper_outputs/tables/tab_app_scope_params.tex
-  tab:fireprone_contract_progression:
-    output: paper_outputs/tables/tab_fireprone_contract_progression.tex
-    inputs:
-      - artifacts/results/fireprone_contract_progression_summary.json
-  tab:appendix_selection_regret_tolerance:
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-    inputs:
-      - artifacts/results/selection_regret_all_backbones_20260504.csv
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-    inputs:
-      - artifacts/results/fireprone_contract_progression_summary.json
-  tab:app_spread_ap_by_scope:
-    output: paper_outputs/tables/tab_app_spread_ap_by_scope.tex
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-    output: paper_outputs/tables/tab_app_seed_robustness.tex
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-    output: paper_outputs/tables/tab_app_head_architectures.tex

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artifacts/results/fireprone_contract_progression_summary.json CHANGED Viewed

@@ -1106,7 +1106,7 @@
       "source_priority": 0
     },
     {
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       "label": "Pangu-Weather",
       "seed": 1,
       "scope": "full_domain",
@@ -1124,7 +1124,7 @@
       "source_priority": 0
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artifacts/results/selection_regret_all_backbones_20260504.csv ADDED Viewed

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artifacts/results/selection_regret_all_backbones_20260504.json ADDED Viewed

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

artifacts/results/selection_regret_scope_sweep_20260505.csv CHANGED Viewed

@@ -1,45 +1,45 @@
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-fuxi,FuXi,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-fuxi,FuXi,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.002973545331200933,0.0023946274991058026,0.0010927807990139538,0.007024214143542151,0.013545122545609134,0.02097023683418404,0.0,0.050156261654859424,0.013545122545609134,0.02097023683418404,0.0,0.050156261654859424
-fuxi,FuXi,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.001383793743586542,0.0019248128430711165,0.0,0.003938013087198336,0.0016559834970027332,0.0037028916689159307,0.0,0.008279917485013666,0.0016559834970027332,0.0037028916689159307,0.0,0.008279917485013666
-fuxi,FuXi,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.00283318355751887,0.0036808289681375247,0.0,0.008746323525994693,0.00283318355751887,0.0036808289681375247,0.0,0.008746323525994693
-pangu6,Pangu-Weather,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-pangu6,Pangu-Weather,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.003154674487241463,0.002546125713211599,0.0,0.005711594157251587,0.007592888149777122,0.00897418737588444,0.0,0.019790633919317124,0.007592888149777122,0.00897418737588444,0.0,0.019790633919317124
-pangu6,Pangu-Weather,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.0017345627303725214,0.0019305189318827886,0.0,0.004535321555179647,0.003047840737004992,0.005053805614558161,0.0,0.011660780793438352,0.003047840737004992,0.005053805614558161,0.0,0.011660780793438352
-pangu6,Pangu-Weather,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0007280423771922354,0.001178746460551365,0.0,0.0027096086413018403,0.0018679847512695024,0.0032548337047755126,0.0,0.007639684811918013,0.0018679847512695024,0.0032548337047755126,0.0,0.007639684811918013
-alphaearth,AlphaEarth,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.1722171037486726,0.08849214830495522,0.050655329891645895,0.272468682523694,0.1722171037486726,0.08849214830495522,0.050655329891645895,0.272468682523694
-alphaearth,AlphaEarth,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.06384618090125256,0.04965276403138872,0.0,0.1365277562230962,0.06384618090125256,0.04965276403138872,0.0,0.1365277562230962
-alphaearth,AlphaEarth,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.06573776411084173,0.06897015340160571,0.0,0.1615566566666954,0.06573776411084173,0.06897015340160571,0.0,0.1615566566666954
-alphaearth,AlphaEarth,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.038803552655092256,0.0594825313313219,0.0,0.13482302181308625,0.038803552655092256,0.0594825313313219,0.0,0.13482302181308625

+model_tag,label,scope,scope_label,n,seeds,exact_regret_mean,exact_regret_std,exact_regret_min,exact_regret_max,tolerated_regret_mean,tolerated_regret_std,tolerated_regret_min,tolerated_regret_max,union_regret_mean,union_regret_std,union_regret_min,union_regret_max
+reference,FireWx-FM ref.,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.07383089600948442,0.07453636071372995,0.0,0.17497107865629713,0.07383089600948442,0.07453636071372995,0.0,0.17497107865629713
+reference,FireWx-FM ref.,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.003663718115532055,0.006812231244812292,0.0,0.015676762201120686,0.003663718115532055,0.006812231244812292,0.0,0.015676762201120686
+reference,FireWx-FM ref.,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.012275489592085752,0.012665162001740834,0.0,0.02670880922526031,0.012275489592085752,0.012665162001740834,0.0,0.02670880922526031
+reference,FireWx-FM ref.,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.029384646387840017,0.02751315335001922,0.0,0.05675140203555318,0.029384646387840017,0.02751315335001922,0.0,0.05675140203555318
+prithvi_wxc,Prithvi-WxC,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+prithvi_wxc,Prithvi-WxC,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+prithvi_wxc,Prithvi-WxC,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+prithvi_wxc,Prithvi-WxC,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+aurora,Aurora,global,\(\Omega=\)global,5,1 7 42 99 123,0.00010153879814819402,0.00021861477435572763,0.0,0.0004925501476206198,0.04945471159670635,0.10697394238964528,0.0,0.240793572992212,0.04945471159670635,0.10697394238964528,0.0,0.240793572992212
+aurora,Aurora,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.023667154505540456,0.05292136630837888,0.0,0.11833577252770228,0.1542829840966487,0.34498724021162547,0.0,0.7714149204832434,0.1542829840966487,0.34498724021162547,0.0,0.7714149204832434
+aurora,Aurora,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.014651279478173606,0.032761256870543834,0.0,0.07325639739086803,0.1399343063221699,0.31290262132065055,0.0,0.6996715316108496,0.1399343063221699,0.31290262132065055,0.0,0.6996715316108496
+aurora,Aurora,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.008202508825959588,0.01834136732088763,0.0,0.04101254412979794,0.1437064941446237,0.32133748971555404,0.0,0.7185324707231184,0.1437064941446237,0.32133748971555404,0.0,0.7185324707231184
+climax,ClimaX,global,\(\Omega=\)global,5,1 7 42 99 123,3.0287686240700486e-06,4.147312242167625e-06,0.0,7.571921560175121e-06,0.0012959969982639485,0.0017746169760203706,0.0,0.0032399924956598714,0.0012959969982639485,0.0017746169760203706,0.0,0.0032399924956598714
+climax,ClimaX,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+climax,ClimaX,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+climax,ClimaX,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+stormcast,StormCast,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+stormcast,StormCast,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+stormcast,StormCast,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+stormcast,StormCast,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+dlwp,DLWP,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+dlwp,DLWP,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0048037709215293075,0.006217185202152866,0.0,0.015203005078871956,0.016716228534155796,0.016079313546074458,0.0,0.03305057342744666,0.016716228534155796,0.016079313546074458,0.0,0.03305057342744666
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+fcn,FCN,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+fcn,FCN,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0006342898232943345,0.0009899554165032742,0.0,0.002257520679520411,0.004509624980300697,0.010070611656609236,0.0,0.022524473456150496,0.004509624980300697,0.010070611656609236,0.0,0.022524473456150496
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+fengwu,FengWu,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+fengwu,FengWu,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0005029843170376968,0.0008109166521114917,0.0,0.0018628094907809783,0.008795951947678215,0.005532321338017505,0.0,0.01484136735033148,0.008795951947678215,0.005532321338017505,0.0,0.01484136735033148
+fengwu,FengWu,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.000495228089292582,0.0007349190216431337,0.0,0.0016387212062008855,0.00402300475984525,0.005510851442075993,0.0,0.010273937098576491,0.00402300475984525,0.005510851442075993,0.0,0.010273937098576491
+fengwu,FengWu,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0006908222234409067,0.0011910586589384115,0.0,0.0027505832409660327,0.005222389249812243,0.0062394095558402415,0.0,0.015150501067724198,0.005222389249812243,0.0062394095558402415,0.0,0.015150501067724198
+fuxi,FuXi,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+fuxi,FuXi,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.002973545331200933,0.0023946274991058026,0.0010927807990139538,0.007024214143542151,0.013545122545609134,0.02097023683418404,0.0,0.050156261654859424,0.013545122545609134,0.02097023683418404,0.0,0.050156261654859424
+fuxi,FuXi,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.001383793743586542,0.0019248128430711165,0.0,0.003938013087198336,0.0016559834970027332,0.0037028916689159307,0.0,0.008279917485013666,0.0016559834970027332,0.0037028916689159307,0.0,0.008279917485013666
+fuxi,FuXi,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.00283318355751887,0.0036808289681375247,0.0,0.008746323525994693,0.00283318355751887,0.0036808289681375247,0.0,0.008746323525994693
+pangu6,Pangu-Weather,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+pangu6,Pangu-Weather,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.003154674487241463,0.002546125713211599,0.0,0.005711594157251587,0.007592888149777122,0.00897418737588444,0.0,0.019790633919317124,0.007592888149777122,0.00897418737588444,0.0,0.019790633919317124
+pangu6,Pangu-Weather,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.0017345627303725214,0.0019305189318827886,0.0,0.004535321555179647,0.003047840737004992,0.005053805614558161,0.0,0.011660780793438352,0.003047840737004992,0.005053805614558161,0.0,0.011660780793438352
+pangu6,Pangu-Weather,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0007280423771922354,0.001178746460551365,0.0,0.0027096086413018403,0.0018679847512695024,0.0032548337047755126,0.0,0.007639684811918013,0.0018679847512695024,0.0032548337047755126,0.0,0.007639684811918013
+alphaearth,AlphaEarth,global,\(\Omega=\)global,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.1722171037486726,0.08849214830495522,0.050655329891645895,0.272468682523694,0.1722171037486726,0.08849214830495522,0.050655329891645895,0.272468682523694
+alphaearth,AlphaEarth,top5,\(\Omega=\)top 5\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.06384618090125256,0.04965276403138872,0.0,0.1365277562230962,0.06384618090125256,0.04965276403138872,0.0,0.1365277562230962
+alphaearth,AlphaEarth,top10,\(\Omega=\)top 10\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.06573776411084173,0.06897015340160571,0.0,0.1615566566666954,0.06573776411084173,0.06897015340160571,0.0,0.1615566566666954
+alphaearth,AlphaEarth,top20,\(\Omega=\)top 20\%,5,1 7 42 99 123,0.0,0.0,0.0,0.0,0.038803552655092256,0.0594825313313219,0.0,0.13482302181308625,0.038803552655092256,0.0594825313313219,0.0,0.13482302181308625

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-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9090,11 +9090,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9132,11 +9132,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9174,11 +9174,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9216,11 +9216,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9258,11 +9258,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9300,11 +9300,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9342,11 +9342,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9384,11 +9384,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9426,11 +9426,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9468,11 +9468,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9510,11 +9510,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9552,11 +9552,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9594,11 +9594,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9636,11 +9636,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9678,11 +9678,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9720,11 +9720,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9762,11 +9762,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9804,11 +9804,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9846,11 +9846,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9888,11 +9888,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9930,11 +9930,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -9972,11 +9972,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -10014,11 +10014,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -10056,11 +10056,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -10098,11 +10098,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -10140,11 +10140,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,
@@ -10182,11 +10182,11 @@
         123
       ],
       "paths": [
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>",
-        "<local paths omitted>"
       ],
       "exact_regret": {
         "n": 5,

         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,
         123
       ],
       "paths": [
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>",
+        "<local path omitted>"
       ],
       "exact_regret": {
         "n": 5,

assets/{primary_rank_change_preview.png → overview_final.png} RENAMED Viewed

File without changes

assets/{selection_regret_preview.png → primary_rank_change_final.png} RENAMED Viewed

File without changes

assets/release_contents.svg ADDED Viewed

assets/{task_rank_map_preview.png → selection_regret_final.png} RENAMED Viewed

File without changes

assets/supporting_rank_map_final.png ADDED Viewed

Git LFS Details

SHA256: 34807e65ca71365a26a3b74cae70e6b40ae6f2151110e12c53e0efa9f8b726aa
Pointer size: 131 Bytes
Size of remote file: 697 kB

assets/wildfire_fm_model_card.svg ADDED Viewed

docs/huggingface_release_design.md CHANGED Viewed

@@ -1,16 +1,27 @@
 # Hugging Face Release Design
-This release follows the common Hugging Face pattern for research artifacts:
-- `README.md` is the public card. It contains YAML metadata, intended use,
-  limitations, data provenance, reproduction commands, and citation text.
-- `paper_outputs/` stores the final TeX, TikZ, and PDF artifacts used by the
   manuscript.
 - `artifacts/results/` stores compact CSV/JSON summaries that can be public.
-- `artifacts/manifests/` maps paper labels to files and records output hashes.
 - `data_sources/` documents external data resources without redistributing them.
 - `experiments/` contains raw-rerun reference scripts and Slurm templates.
-The repository is intentionally a paper-artifact release rather than a dataset
-mirror or model-weight release. Full raw-data reruns require separately obtained
-source data and local feature caches.

 # Hugging Face Release Design
+This release follows a model-card-first pattern used by geospatial and weather
+foundation-model releases on the Hub. The public page starts with model purpose,
+released weights, input expectations, a quick loading example, data boundaries,
+and a compact evaluation snapshot. Paper artifacts are included as supporting
+reproducibility material rather than being the main organizing principle.
+## Public Card Structure
+- `README.md` is the public model card. It contains YAML metadata, intended use,
+  checkpoint locations, quick loading code, data-source boundaries, limitations,
+  and citation text.
+- `assets/` contains lightweight visuals for the Hub page plus PNG previews of
+  final-paper figures.
+- `models/wildfire_fm/` contains model code, manifests, and checkpoint metadata.
+- `paper_outputs/` stores final TeX, TikZ, and PDF artifacts used by the
   manuscript.
 - `artifacts/results/` stores compact CSV/JSON summaries that can be public.
 - `data_sources/` documents external data resources without redistributing them.
 - `experiments/` contains raw-rerun reference scripts and Slurm templates.
+## Data Boundary
+The repository is a model release with reproducibility artifacts, not a raw-data
+mirror. Full raw-data reruns require separately obtained source data, local
+feature caches, and cluster-specific paths.

scripts/audit_release.py CHANGED Viewed

@@ -25,9 +25,11 @@ REQUIRED = [
     "paper_outputs/figures/fig_primary_rank_change_map.pdf",
     "paper_outputs/figures/fig_selection_regret_scatter.pdf",
     "paper_outputs/figures/fig_rank_heatmap1.pdf",
-    "assets/selection_regret_preview.png",
-    "assets/task_rank_map_preview.png",
-    "assets/primary_rank_change_preview.png",
     "artifacts/manifests/paper_outputs.sha256",
     "scripts/check_paper_output_hashes.py",
 ]
@@ -84,7 +86,7 @@ def main() -> None:
                 issues.append(f"{path.relative_to(ROOT)} contains forbidden token {token!r}")
     readme = (ROOT / "README.md").read_text(errors="ignore")
-    for phrase in ["WildFIRE-FM", "Model weights", "Data Used By The Study", "Loading A Checkpoint"]:
         if phrase not in readme:
             issues.append(f"README missing expected model-card phrase: {phrase}")

     "paper_outputs/figures/fig_primary_rank_change_map.pdf",
     "paper_outputs/figures/fig_selection_regret_scatter.pdf",
     "paper_outputs/figures/fig_rank_heatmap1.pdf",
+    "assets/wildfire_fm_model_card.svg",
+    "assets/release_contents.svg",
+    "assets/selection_regret_final.png",
+    "assets/supporting_rank_map_final.png",
+    "assets/primary_rank_change_final.png",
     "artifacts/manifests/paper_outputs.sha256",
     "scripts/check_paper_output_hashes.py",
 ]
                 issues.append(f"{path.relative_to(ROOT)} contains forbidden token {token!r}")
     readme = (ROOT / "README.md").read_text(errors="ignore")
+    for phrase in ["WildFIRE-FM", "Quick Load", "Data Sources", "Evaluation Snapshot"]:
         if phrase not in readme:
             issues.append(f"README missing expected model-card phrase: {phrase}")