| { |
| "model_name": "CESM-SeasonalML", |
| "model_type": "cesm_seasonal_ml", |
| "architectures": ["CESMSeasonalML"], |
| "architecture_metadata": { |
| "neural_variants": ["FeedForwardNN", "SeasonalLSTM"], |
| "classical_models": ["NumpyRandomForest", "NumpyGradientBoostedTrees"] |
| }, |
| "framework": "PyTorch and NumPy", |
| "domain": "earth-science", |
| "task": "NDJ/JFM four-class seasonal precipitation forecast", |
| "paper": { |
| "title": "Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts", |
| "doi": "10.1038/s43247-021-00225-4", |
| "article_license": "CC BY 4.0" |
| }, |
| "contracts": { |
| "rf_xgb_input": ["B", 103], |
| "nn_input": ["B", 416], |
| "lstm_ndj_input": ["B", 4, 28], |
| "lstm_jfm_input": ["B", 12, 28], |
| "target": ["B"], |
| "classes": 4 |
| }, |
| "implementation": { |
| "entry_point": "model/cesm_seasonal_ml.py", |
| "train_script": "scripts/train.py", |
| "inference_script": "scripts/inference.py", |
| "evaluation_script": "scripts/result.py", |
| "synthetic_data_script": "scripts/fake_data.py" |
| }, |
| "configuration_sources": ["conf/config.yaml", "model/cesm_seasonal_ml.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"], |
| "known_conflicts": [ |
| "The paper reports 103 RF/XGB predictors but does not provide a complete machine-readable feature manifest that closes exactly to 103.", |
| "The last 80 NN dimensions are uniquely named engineering_interaction_* and are a dimension-preserving engineering assumption, not a paper fact.", |
| "The paper does not state the final western-US precipitation grid dimensions after preprocessing." |
| ] |
| } |
|
|
