| { |
| "task_id": "capecod_plume_reconstruction", |
| "name": "Capecod Plume Reconstruction", |
| "category": "Scientific Problems & ML", |
| "base_image": "python", |
| "platform": "linux/amd64", |
| "internet": false, |
| "cwd": "/home/workspace/capecod_plumebench", |
| "submit_paths": [ |
| "model.py", |
| "predict.py", |
| "monitoring_plan.py", |
| "baseline_solver.py", |
| "predictions.csv", |
| "plume_metrics.json", |
| "monitoring_plan.json", |
| "answer.json", |
| "report.md" |
| ], |
| "submit_exclude": [ |
| "data/", |
| "schemas/", |
| "hidden/", |
| "scoring/", |
| "__pycache__/" |
| ], |
| "work": { |
| "image_tag": "d051446beb3a", |
| "specs_dir": "/home/workspace/capecod_plumebench", |
| "agent_query": "# CapeCod-PlumeBench: Groundwater Plume Reconstruction and Monitoring Design\n\nYou are a groundwater remediation modeling engineer. The workspace contains partial public monitoring\ndata from an offline benchmark inspired by the USGS Cape Cod treated-wastewater groundwater plume.\n\nYour job is to replace the weak baseline with a better, defensible modeling workflow that:\n\n1. Cleans and interprets public well, chemistry, and site-configuration data.\n2. Predicts hidden well/year/analyte concentrations listed in `data/prediction_requests.csv`.\n3. Estimates plume metrics requested in `data/plume_metric_requests.csv`.\n4. Proposes up to 8 next monitoring wells under the budget in `data/public_site_config.json`.\n5. Writes a concise technical report explaining your model, assumptions, validation, and monitoring logic.\n\nRequired outputs:\n\n- `model.py`\n- `predict.py`\n- `monitoring_plan.py`\n- `predictions.csv`\n- `plume_metrics.json`\n- `monitoring_plan.json`\n- `answer.json`\n- `report.md`\n\nRun `python baseline_solver.py` to regenerate outputs before submitting. The current baseline is valid\nbut intentionally weak. Improve the model rather than only editing output files. Good solutions use\nspace-time structure, analyte-specific behavior, plume-front geometry, censored observations, and\nuncertainty-aware monitoring selection.\n\nRules:\n\n- Do not attempt to read hidden judge or scoring files.\n- Do not hard-code target truth values or hidden candidate utilities.\n- Keep all outputs finite, nonnegative, and schema-compliant.\n- Respect the monitoring budget and maximum number of wells.\n" |
| }, |
| "judge": { |
| "image_tag": "f633dd04bd60", |
| "eval_cmd": "python /opt/capecod_scoring/evaluate.py", |
| "eval_timeout": 180, |
| "parser": "score_sum", |
| "score_direction": "maximize", |
| "selection": "score_first" |
| } |
| } |
|
|
