| { |
| "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"] |
| } |
|
|
