{
  "model_name": "ML-MODIS",
  "model_type": "ml_modis",
  "architectures": ["BootstrapRandomForestRegressor"],
  "framework": "PyTorch",
  "domain": "earth-science",
  "task": "counterfactual-cloud-property-regression",
  "implementation": {
    "entry_point": "model/ml_modis.py",
    "scope": "independent paper-method engineering reproduction",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "family": "bootstrap random regression forest",
    "input_features": 114,
    "output_targets": 4,
    "independent_models": 8,
    "months": [9, 10],
    "targets": ["Nd", "reff", "LWP", "CF"]
  },
  "data": {
    "format": "NPZ",
    "protocol": "ml_modis_npz_v1",
    "input_shape": ["N", 114],
    "target_shape": ["N", 4],
    "alignment_key": ["year", "month", "platform", "latitude", "longitude"]
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/ml_modis.py",
    "scripts/fake_data.py",
    "scripts/train.py",
    "scripts/inference.py",
    "scripts/result.py"
  ]
}