{
  "model_name": "DINCAE",
  "model_type": "dincae",
  "architectures": ["DINCAE"],
  "framework": "PyTorch",
  "domain": "ocean",
  "task": "probabilistic-sst-gap-reconstruction",
  "implementation": {
    "entry_point": "model/dincae.py",
    "scope": "core-method reduced-spatial-scale engineering reproduction with paper dimensions recorded separately",
    "train_script": "scripts/train.py",
    "inference_script": "scripts/inference.py",
    "evaluation_script": "scripts/result.py",
    "synthetic_data_script": "scripts/fake_data.py"
  },
  "architecture": {
    "engineering_input_shape": ["B", 10, 32, 32],
    "engineering_output_shape": ["B", 2, 32, 32],
    "paper_input_shape": ["B", 10, 112, 112],
    "paper_output_shape": ["B", 2, 112, 112],
    "engineering_filters": [8, 12, 18, 27],
    "paper_filters": [16, 24, 36, 54],
    "paper_bottleneck": [2646, 529, 2646],
    "decoder": "nearest-neighbor upsampling with encoder skips"
  },
  "data": {
    "dataset": "AVHRR Pathfinder daily SST",
    "format_version": "dincae_avhrr_v1",
    "paper_period": "1985-2009",
    "paper_time_steps": 5266,
    "paper_grid": [112, 112],
    "engineering_grid": [32, 32],
    "input_channels": 10,
    "output_channels": 2,
    "synthetic": true
  },
  "configuration_sources": [
    "conf/config.yaml",
    "model/dincae.py",
    "scripts/fake_data.py",
    "scripts/train.py",
    "scripts/inference.py",
    "scripts/result.py"
  ]
}