{"id":"W2017747083","doi":"10.1139/s03-055","title":"Modeling coliforms in storm water plumes","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Environmental Protection Agency","keywords":"Environmental science; Indicator bacteria; Surface runoff; Fecal coliform; Stormwater; Water quality; Hydrology (agriculture); Storm; Combined sewer; Outfall; Turbidity; Estuary; Environmental engineering; Ecology; Oceanography; Meteorology; Geography; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002940648,0.0005795542,0.0004209765,0.0003199589,0.0004327169,0.0007204737,0.0005906732,0.00102679,0.0006766897],"category_scores_gemma":[0.0009626591,0.0004441258,0.0006679186,0.0002380457,0.0005111787,0.0006753432,0.000437547,0.0004952139,0.00009326245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001277071,"about_ca_system_score_gemma":0.001414818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09056301,"about_ca_topic_score_gemma":0.03636893,"domain_scores_codex":[0.9999045,0.00002015697,0.000005621022,0.00002303843,0.00001742561,0.00002923475],"domain_scores_gemma":[0.9997628,0.0001039279,0.00004080494,0.00001054204,0.00005262443,0.00002921542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008603723,0.00002060899,0.002947606,0.000004206541,0.000005278731,0.00003042878,0.00001848955,0.9947621,0.0009516803,0.0003497599,0.00003721211,0.0008639345],"study_design_scores_gemma":[0.000003711929,0.00001016042,0.0003375598,5.857149e-7,0.000002095131,0.000003591027,0.00000699765,0.9992077,0.0002421873,0.0001310376,0.00005270648,0.00000170727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9491981,0.00008952172,0.04840983,0.0002048934,0.00002283467,0.0000469724,0.000188316,0.0001748145,0.001664678],"genre_scores_gemma":[0.9928789,0.00008350398,0.005349257,0.00001866856,0.000008478617,0.00003804073,0.000141453,0.00001742928,0.001464258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09056301,"threshold_uncertainty_score":0.1800717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004353127995322559,"score_gpt":0.1715007434382597,"score_spread":0.1671476154429371,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}