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b300acf 3dcf5c7 b300acf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"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."
]
}
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