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f1720ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"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"
}
}
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