{
  "model_name": "CausalModelEvaluation",
  "model_type": "causalmodelevaluation",
  "architectures": ["LaggedPartialCorrelationCME", "PrecipitationConstraintGP"],
  "framework": "PyTorch/NumPy/scikit-learn",
  "domain": "climate-science",
  "task": "causal-network-model-evaluation-and-constrained-precipitation-projection",
  "implementation": {
    "entry_point": "model/causalmodelevaluation.py",
    "scope": "linear PCMCI-ParCorr engineering approximation at the paper node and lag dimensions",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "family": "lagged partial correlation conditional regression and Gaussian process",
    "nodes": 50,
    "maximum_lag_steps": 10,
    "time_step_days": 3,
    "paper_significance_threshold": 0.0001,
    "engineering_fake_data_threshold": 0.02,
    "network_layout": ["source", "target", "lag"],
    "network_shape": [50, 50, 10]
  },
  "data": {
    "protocol": "cme_structured_var_seasonal_v1",
    "node_series_layout": ["member", "three_day_step", "node"],
    "node_series_shape": ["B", 2100, 50],
    "years_per_segment": 70,
    "seasons": ["DJF", "MAM", "JJA", "SON"],
    "precipitation_grid_shape": [73, 144],
    "precipitation_grid_degrees": 2.5,
    "network_output_shape": [50, 50, 10],
    "model_count": 4
  },
  "configuration_sources": ["conf/config.yaml", "model/causalmodelevaluation.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"]
}