{
  "model_name": "ClimEmu-S2L",
  "model_type": "climemu_s2l",
  "architectures": ["DualRidge", "SharedKernelGPR"],
  "framework": "PyTorch",
  "domain": "earth-science",
  "task": "short-to-long-term-climate-response",
  "implementation": {
    "entry_point": "model/climemu_s2l.py",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "data": {
    "scenarios": 21,
    "grid": [145, 192],
    "features_per_scenario": 27840,
    "input_channels": 1,
    "outer_validation": "21-fold leave-one-scenario-out"
  },
  "methods": {
    "ridge": "multi-output sample-space dual closed form with internal 3-fold alpha selection",
    "gpr": "multi-output posterior mean with one shared non-ARD kernel and optimized marginal likelihood",
    "kernel_modes": ["paper_rbf", "official_rbf_linear"]
  },
  "paper": {
    "title": "Predicting global patterns of long-term climate change from short-term simulations using machine learning",
    "doi": "10.1038/s41612-020-00148-5",
    "data_doi": "10.5281/zenodo.3971024",
    "license": "CC BY 4.0"
  },
  "configuration_sources": ["conf/config.yaml", "model/climemu_s2l.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"]
}