Test FireWx-FM upload permission

README.md

@@ -5,422 +5,105 @@ tags:
   - geospatial
   - weather
   - earth-observation
-  - foundation-models
-  - evaluation
   - pytorch
+  - reproducibility
 pipeline_tag: image-segmentation
 library_name: pytorch
-pretty_name: WildFIRE-FM
+pretty_name: FireWx-FM
 ---
 
-<div align="center">
+# FireWx-FM
 
-<h1 align="center">WildFIRE-FM</h1>
+FireWx-FM is a wildfire-focused gridded model for 12-hour active-fire occupancy
+prediction over the Lower 48 United States. The released code maps a fixed
+16-channel tensor on a 5 km EPSG:5070 grid to an active-fire occupancy
+probability map.
 
-<h3 align="center">A wildfire-specialized reference backbone for fixed-contract Earth-FM transfer evaluation</h3>
+This Hugging Face release is the clean ICLR/technical-report companion package:
+it includes model code, checkpoints, inference utilities, training/cache
+templates, public data-source notes, example outputs, the ICLR manuscript
+source, and the technical report source.
 
-<p align="center"><b>Five seeded California regional PyTorch checkpoints, paper-aligned evaluation artifacts, final figure previews, source-data access notes, and a CONUS retraining recipe for 12-hour wildfire occupancy prediction.</b></p>
+## Contents
 
-<p>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/resolve/main/paper/wildfire_fm_evaluation_contracts.pdf"><img alt="Paper PDF" src="https://img.shields.io/badge/Paper-compiled%20PDF-0F5C5F?style=flat-square"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/models/wildfire_fm/checkpoints"><img alt="Checkpoints" src="https://img.shields.io/badge/Checkpoints-5%20seeds-C66B2D?style=flat-square"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/models/wildfire_fm/modeling_unet.py"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-model%20code-EE4C2C?style=flat-square&logo=pytorch&logoColor=white"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/data_sources/DATA_SOURCES.md"><img alt="Data notes" src="https://img.shields.io/badge/Data-source%20notes-536065?style=flat-square"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/artifacts/results"><img alt="Results" src="https://img.shields.io/badge/Results-sanitized%20summaries-0F5C5F?style=flat-square"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/assets"><img alt="Figures" src="https://img.shields.io/badge/Figures-final%20previews-C66B2D?style=flat-square"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/data_sources/DATA_SOURCES.md"><img alt="Grid" src="https://img.shields.io/badge/Grid-5%20km%20EPSG%3A5070-0F5C5F?style=flat-square"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#quick-start"><img alt="Input" src="https://img.shields.io/badge/Input-16%20channels-C66B2D?style=flat-square"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#task-contract-snapshot"><img alt="Tasks" src="https://img.shields.io/badge/Contracts-6%20task%20views-536065?style=flat-square"></a>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-MIT-0F5C5F?style=flat-square"></a>
-</p>
-
-<p>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/paper/wildfire_fm_evaluation_contracts.pdf"><b>Read the paper PDF</b></a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/models/wildfire_fm/checkpoints"><b>Download checkpoints</b></a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/training"><b>Training/data loader</b></a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/spatial_serving"><b>Custom spatial serving</b></a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#quick-start"><b>Load the model</b></a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#task-contract-snapshot"><b>Inspect task-contract results</b></a>
-</p>
-
-<h3>5 California Seeded Checkpoints · 16-Channel Gridded Input · 12-Hour Occupancy Lead · CONUS Retraining Recipe</h3>
-
-<p>
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#why-wildfire-fm">Why WildFIRE-FM</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#release-navigation">Release Navigation</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#visual-tour">Visual Tour</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#quick-start">Quick Start</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/training">Training Pipeline</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/spatial_serving">Spatial Serving</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/training/NATIONWIDE_RETRAINING.md">CONUS Retraining</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/docs/hugh_handoff_status.md">Integration Handoff</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#task-contract-snapshot">Task Snapshot</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#data-sources">Data Sources</a> ·
-  <a href="https://huggingface.co/RAI-Lab/Wildfire-FM#how-to-cite">How to Cite</a>
-</p>
-
-</div>
-
----
-
-<p align="center">
-  <img src="https://huggingface.co/RAI-Lab/Wildfire-FM/resolve/main/assets/wildfire_fm_model_card.svg" alt="WildFIRE-FM overview" width="95%">
-</p>
-
-## Release Navigation
-
-<table>
-  <tr>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/paper/wildfire_fm_evaluation_contracts.pdf">Paper PDF</a></h3>
-      <p>Read the compiled manuscript that defines the fixed-contract wildfire transfer setting and reports the final results.</p>
-    </td>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/models/wildfire_fm/checkpoints">Model Checkpoints</a></h3>
-      <p>Access five seeded WildFIRE-FM weights with manifest metadata and SHA-256 hashes for release auditing.</p>
-    </td>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/models/wildfire_fm/modeling_unet.py">Model Code</a></h3>
-      <p>Inspect the compact U-Net implementation used to load the released wildfire occupancy backbone.</p>
-    </td>
-  </tr>
-  <tr>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/training">Training/Data Loader</a></h3>
-      <p>Read the original cache builder, tensor assembly, tile sampler, full-map loader, and training loop.</p>
-    </td>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/spatial_serving">Custom Spatial Serving</a></h3>
-      <p>Aggregate native grid probabilities to any user-defined polygon granularity.</p>
-    </td>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM#quick-start">Quick Start</a></h3>
-      <p>Clone the repository, instantiate the model, load a seeded checkpoint, and run the artifact check.</p>
-    </td>
-  </tr>
-  <tr>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM#task-contract-snapshot">Task Snapshot</a></h3>
-      <p>View the final-paper task-contract summary for occupancy, spread, retrieval, burned area, smoke, and heat.</p>
-    </td>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM/tree/main/artifacts/results">Numeric Artifacts</a></h3>
-      <p>Use sanitized CSV and JSON summaries that back the public model-card tables and figure previews.</p>
-    </td>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM#visual-tour">Visual Tour</a></h3>
-      <p>Browse final-paper previews for matching-rule sensitivity, selection regret, and task-form ranking changes.</p>
-    </td>
-  </tr>
-  <tr>
-    <td width="33%" valign="top">
-      <h3><a href="https://huggingface.co/RAI-Lab/Wildfire-FM/blob/main/data_sources/DATA_SOURCES.md">Data Sources</a></h3>
-      <p>See the public provider links and roles for HRRR, FIRMS, LANDFIRE, WRC, LandScan, WFIGS, and MTBS.</p>
-    </td>
-  </tr>
-</table>
-
----
-
-## Why WildFIRE-FM
-
-Wildfire Earth-FM transfer scores depend strongly on the contract used for comparison: task form, metric, matching rule, spatial scope, and head-selection criterion. The paper studies these choices by holding outputs or features fixed and then changing only the evaluation contract.
-
-WildFIRE-FM is the in-region reference model used in those comparisons. It is trained for 12-hour gridded wildfire occupancy on a California 5 km grid, then evaluated under the same task-specific contracts as the transferred Earth-FM backbones.
-
-The released weights are California regional checkpoints, not nationwide-trained weights. For nationwide use, the release includes a CONUS cache and training recipe under `training/NATIONWIDE_RETRAINING.md`.
-
-This Hub repository is model-first. It provides the released weights, model-loading code, compact result summaries, paper previews, and source-data access notes needed to inspect the release boundary before running larger local reproductions.
-
----
-
-## Key Features
-
-- **Wildfire-specialized reference model:** Compact U-Net for 12-hour occupancy prediction on a projected California grid.
-- **Five seeded checkpoints:** Seeds `1`, `7`, `42`, `99`, and `123` are released with manifest hashes.
-- **Fixed-contract artifacts:** Compact summaries cover matching-rule, head-selection, and task-form comparisons from the final paper.
-- **Source-data aware release:** Provider links and data roles are documented for each public resource used by the study.
-- **Compiled paper PDF:** The readable manuscript is available directly from the Hub release.
-- **Paper-aligned previews:** Figure assets summarize selection regret, supporting-task rank changes, and primary-task rank changes.
-
-<table>
-  <tr>
-    <td width="50%" valign="top">
-      <h3>Reference model for wildfire transfer</h3>
-      <p>WildFIRE-FM combines regional weather, active-fire supervision, and static fuel, canopy, housing, and population context on the same grid used by the occupancy contract.</p>
-    </td>
-    <td width="50%" valign="top">
-      <h3>Contract-first evaluation artifacts</h3>
-      <p>The released summaries keep metric roles separate across decision, overlap, ranking, regression, and supporting-task checks.</p>
-    </td>
-  </tr>
-  <tr>
-    <td width="50%" valign="top">
-      <h3>Local-data reproducibility path</h3>
-      <p>Full raw-data reruns use provider-hosted sources and local feature caches. The Hub release documents those inputs and provides lightweight artifact checks.</p>
-    </td>
-    <td width="50%" valign="top">
-      <h3>Final-paper result previews</h3>
-      <p>The model card highlights the paper's fixed-output, fixed-feature, and task-form findings without requiring manuscript source files.</p>
-    </td>
-  </tr>
-</table>
-
----
-
-## Visual Tour
-
-<p align="center">
-  <img src="https://huggingface.co/RAI-Lab/Wildfire-FM/resolve/main/assets/release_contents.svg" alt="Release contents" width="92%">
-</p>
-
-**Release contents.** The Hub repository is organized around weights, model code, source-data notes, compact artifacts, and final-paper visual previews.
-
-<p align="center">
-  <img src="https://huggingface.co/RAI-Lab/Wildfire-FM/resolve/main/assets/selection_regret_final.png" alt="Head-selection regret" width="88%">
-</p>
-
-**Head-selection regret.** Choosing a lightweight head by a ranking metric can lose decision performance under the same frozen features.
-
-<p align="center">
-  <img src="https://huggingface.co/RAI-Lab/Wildfire-FM/resolve/main/assets/supporting_rank_map_final.png" alt="Supporting task rank map" width="88%">
-</p>
-
-**Supporting-task rank map.** Backbone ordering changes across burned area, analog retrieval, smoke PM2.5, and extreme heat task contracts.
-
-<p align="center">
-  <img src="https://huggingface.co/RAI-Lab/Wildfire-FM/resolve/main/assets/primary_rank_change_final.png" alt="Primary rank changes" width="88%">
-</p>
-
-**Primary-task rank changes.** Primary-task rankings change under fixed matching-rule and task-form choices.
-
----
-
-## At A Glance
-
-| Signal | Release value |
+| Path | Purpose |
 |---|---|
-| Core task | 12-hour gridded wildfire occupancy prediction |
-| Released checkpoint region/grid | California regional grid, 5 km, EPSG:5070 |
-| Nationwide path | CONUS cache and training templates in `training/NATIONWIDE_RETRAINING.md` |
-| Input channels | 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 checkpoints | 5 seeded PyTorch checkpoints |
-| Data access path | Source links and roles documented in `data_sources/` |
-| Training/data loader path | Original cache builder and PyTorch loader in `training/` |
-| Spatial serving path | Custom polygon-granularity aggregation in `spatial_serving/` |
-| Paper artifact path | Compiled PDF in `paper/`; manuscript source managed in the authors' paper workspace |
-
-### Input Channel Contract
-
-The pretrained checkpoints expect a fixed 16-channel tensor in `[channel, y, x]`
-order. Channel order matters.
-Inputs use native source units with no mean/std normalization, min/max scaling,
-or unit conversion. Invalid or missing values are zero-filled, with validity
-masks included as channels 10 and 11.
-
-| Channels | Names | Source |
-|---:|---|---|
-| 0-9 | `t2m`, `d2m`, `u10`, `v10`, `cape`, `sp`, `blh`, `vis`, `prate`, `tp` | NOAA HRRR |
-| 10 | `firewx_valid` | Cache-level dynamic input presence mask; 1.0 everywhere in this release |
-| 11 | `static_valid` | Fraction of static layers valid after reprojection |
-| 12-15 | `fuel_fbfm40`, `canopy_cover`, `housing_density`, `population` | LANDFIRE, WRC housing density, LandScan |
-
-FIRMS provides the occupancy target, not an input channel. WFIGS and MTBS are
-used for supporting event-level tasks and are not part of the 16-channel
-pretrained occupancy input. Full channel-level details are in
-`data_sources/DATA_SOURCES.md`; the same contract is available as
-`models/wildfire_fm/input_channels.json`.
-The `cape` channel is the HRRR surface instantaneous CAPE field selected with
-`typeOfLevel=surface` and `stepType=instant`.
-For validity channels, `firewx_valid` is 1.0 everywhere in the released
-California regional cache and is not a per-variable HRRR missing-data mask;
-`static_valid` is the fraction of the four static layers valid after
-reprojection at each grid cell.
-
----
-
-## Hugging Face Release
-
-This Hub repository is a model release with paper-aligned reproducibility artifacts.
-
-| Folder | Purpose | Example |
-|---|---|---|
-| `models/wildfire_fm/` | Model code, manifests, checkpoint metadata | `models/wildfire_fm/modeling_unet.py` |
-| `models/wildfire_fm/input_channels.json` | Machine-readable 16-channel input contract | `models/wildfire_fm/input_channels.json` |
-| `models/wildfire_fm/checkpoints/` | Five seeded PyTorch checkpoints | `seed_1/best_firms_prauc.pt` |
-| `training/` | Original cache builder, data loader, tile sampler, and training loop | `train_cold_tiled_mainline.py` |
-| `training/NATIONWIDE_RETRAINING.md` | CONUS retraining recipe and data coverage notes | `stage1_cache_conus_hrrr_us_5km_l12_template.json` |
-| `spatial_serving/` | Grid-to-polygon serving adapter for custom spatial granularities | `grid_to_polygons.py` |
-| `data_downloader/hrrr_downloader.py` | Direct NOAA HRRR public archive downloader | `hrrr_download_manifest.json` |
-| `docs/hugh_handoff_status.md` | Integration answers for channel order, masks, normalization, HRRR, and serving boundary | handoff status |
-| `paper/` | Compiled paper PDF | `wildfire_fm_evaluation_contracts.pdf` |
-| `assets/` | Hub-page visuals and final-paper previews | `selection_regret_final.png` |
-| `paper_outputs/figures/` | Selected final-paper figure PDFs | `fig_rank_heatmap1.pdf` |
-| `artifacts/results/` | Sanitized compact numeric summaries | `selection_regret_scope_sweep_20260505.csv` |
-| `data_sources/` | Public data-source roles and access notes | `DATA_SOURCES.md` |
-| `experiments/` | Sanitized raw-rerun references and Slurm template | `slurm/submit_template.sbatch` |
-
-Full local reruns require users to prepare the provider-hosted raw data and comparator assets described in `data_sources/`.
-
----
+| `firewxfm/` | Model definition and tiled CONUS inference code. |
+| `models/checkpoints/` | Released PyTorch checkpoints. |
+| `models/metadata/` | Input-channel contract, normalization statistics, and run metadata. |
+| `training/` | Cache-building, training, and metric utilities. |
+| `training/configs/` | Path-free templates for CONUS and multi-year reruns. |
+| `scripts/` | Data preparation, release checks, live-stack builder, seam audit, and smoke tests. |
+| `examples/final_prediction/` | Small final probability/preview example for inspection. |
+| `data_sources/` | Source-data provenance and access notes. |
+| `paper/iclr_source/` | ICLR manuscript LaTeX source. |
+| `technical_report/source/` | Technical report LaTeX source. |
+| `artifacts/results/` | Compact public result summaries without internal filesystem paths. |
 
 ## Quick Start
 
-Clone the Hub repository or download the files you need:
-
 ```bash
-git clone https://huggingface.co/RAI-Lab/Wildfire-FM
-cd Wildfire-FM
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+python3 scripts/check_release_files.py
+python3 scripts/smoke_inference.py --device cpu
 ```
 
-Load a seeded checkpoint:
+The smoke test uses a synthetic tensor. Full scientific reproduction requires
+the public source datasets listed in `data_sources/DATA_SOURCES.md`.
 
-```python
-import torch
-from models.wildfire_fm.modeling_unet import UNetSmallFlex
+## Model Input Contract
 
-model = UNetSmallFlex(
-    in_ch=16,
-    base=32,
-    dropout=0.1,
-    norm_type="group",
-    norm_groups=8,
-    use_aux_spatial_head=True,
-)
-checkpoint = torch.load(
-    "models/wildfire_fm/checkpoints/seed_1/best_firms_prauc.pt",
-    map_location="cpu",
-)
-state = checkpoint.get("model", checkpoint)
-model.load_state_dict(state)
-model.eval()
-```
+The model expects a channel-first tensor with shape `[16,H,W]`.
 
-Verify the public release artifacts:
-
-```bash
-python3 scripts/reproduce_paper_outputs.py
-```
-
-The checkpoint expects the same fixed-order 16-channel gridded input described
-above and in `data_sources/DATA_SOURCES.md`.
-
----
-
-## Task-Contract Snapshot
-
-The card below reports the best final-paper mean for each displayed task contract, with the winning backbone named in the final column.
-
-| Task contract | Best final-paper mean | Winner |
-|---|---:|---|
-| Occupancy union F1 | `60.1506 ± 7.5865` percent | ClimaX |
-| Fire-spread spatial F1 | `80.9700 ± 2.0200` percent | WildFIRE-FM |
-| Final burned-area log-RMSE | `1.1657 ± 0.0126`, lower is better | WildFIRE-FM |
-| Analog retrieval nDCG@10 | `0.5099 ± 0.0336` | WildFIRE-FM |
-| Smoke PM2.5 RMSE | `4.4403 ± 0.0488`, lower is better | AlphaEarth |
-| Extreme-heat RMSE-C | `0.2179 ± 0.0043`, lower is better | WildFIRE-FM |
-
-The release provides sanitized CSV/JSON summaries used to audit displayed values.
+| Channels | Source | Role |
+|---:|---|---|
+| 0-9 | NOAA HRRR | Dynamic weather fields. |
+| 10-11 | Input builder | Dynamic and static validity masks. |
+| 12 | LANDFIRE | Fire-behavior fuel model. |
+| 13 | LANDFIRE | Canopy cover. |
+| 14-15 | WRC / LandScan | Housing density and population; zeroed in the no-exposure serving run. |
 
----
+See `models/metadata/input_channels.json` and
+`models/metadata/input_normalization_stats.json` for the exact machine-readable
+contract.
 
-## Data Sources
+## Inference
 
-The study uses public or provider-hosted resources. Source roles and access paths are documented in `data_sources/DATA_SOURCES.md`.
+Run inference from a prebuilt 16-channel stack:
 
-- 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 and backbone assets for comparator features.
+```bash
+python3 -m firewxfm.serve_conus \
+  --input-npy /path/to/input_stack_16chw.npy \
+  --checkpoint models/checkpoints/firewxfm_2024_staticfix_region_balanced_bce_seed42.pt \
+  --normalization-stats models/metadata/input_normalization_stats.json \
+  --output-npy outputs/firewxfm_probability.npy
+```
 
----
+For full-map serving, use overlap-window inference with phase averaging. The
+default settings are documented in `docs/serving_notes.md`.
 
-## Repository Layout
+## Reproducibility
 
-```text
-WildFIRE-FM/
-  README.md
-  LICENSE
-  requirements.txt
-  models/wildfire_fm/
-    modeling_unet.py
-    checkpoint_manifest.json
-    checkpoints/seed_*/best_firms_prauc.pt
-  training/
-    build_phase1_cache_regional_hrrr.py
-    train_cold_tiled_mainline.py
-    configs/
-  spatial_serving/
-    grid_to_polygons.py
-  data_downloader/
-    hrrr_downloader.py
-  paper/
-    wildfire_fm_evaluation_contracts.pdf
-  assets/
-    wildfire_fm_model_card.svg
-    release_contents.svg
-    selection_regret_final.png
-    supporting_rank_map_final.png
-    primary_rank_change_final.png
-  paper_outputs/figures/
-    matching.pdf
-    fig_task_contract_tiles.pdf
-    fig_selection_regret_scatter.pdf
-    fig_rank_heatmap1.pdf
-  artifacts/
-    manifests/
-    results/
-  data_sources/
-    DATA_SOURCES.md
-  experiments/
-    raw_reference/
-    slurm/
-  scripts/
-    audit_release.py
-    reproduce_paper_outputs.py
-```
+Use `REPRODUCIBILITY.md` for the exact verification path:
 
----
+- artifact presence check,
+- checkpoint smoke inference,
+- ICLR source compilation,
+- technical report source compilation,
+- real-data cache rebuild and retraining commands.
 
-## How To Cite
+Raw source data are not redistributed. This package documents where each source
+comes from and provides path-free templates for local reproduction.
 
-If you use WildFIRE-FM, the released checkpoints, the fixed-contract evaluation artifacts, or the paper-aligned scripts, please cite:
+## Citation
 
 ```bibtex
-@misc{wildfire_fm_evaluation_contracts_2026,
-  title = {Does Your Wildfire Prediction Model Actually Work, or Just Score Well?},
-  author = {Yangshuang Xu and Yuyang Dai and Liling Chang and Qi Wang and Yushun Dong},
+@misc{firewxfm2026,
+  title = {FireWx-FM: A Wildfire Foundation Model Technical Report},
+  author = {Xu, Yangshuang and Dai, Yuyang and Chang, Liling and Wang, Qi and Dong, Yushun},
   year = {2026},
-  note = {WildFIRE-FM model and fixed-contract wildfire evaluation artifacts}
+  note = {FireWx-FM code, checkpoints, and reproducibility package}
 }
 ```
-
-The citation will be updated with arXiv metadata after the preprint is public.
-
----
-
-## News
-
-- **2026-05-18** - The model card was reorganized with a larger navigation panel and final-paper visual previews.
-- **2026-05-13** - The Hub release added the compiled paper PDF without checklist pages.
-- **2026-05-13** - Five seeded WildFIRE-FM checkpoints were uploaded with SHA-256 metadata.
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-## Scope
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-WildFIRE-FM is scoped to the paper's California regional grid and fixed-contract wildfire transfer comparisons. Use in a new region or grid should start with local preprocessing, validation, and contract-specific evaluation. The release is designed for research inspection, artifact-level checking, and model-loading experiments rather than operational alerting.
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-## License
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-This release is provided under the MIT license. External data and comparator assets remain governed by their original providers and terms of use.
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-WildFIRE-FM is released to make wildfire Earth-FM transfer comparisons easier to inspect, reproduce at the artifact level, and evaluate under explicit contracts.