{"id":"W2004422029","doi":"10.1016/j.watres.2013.02.002","title":"Using Campylobacter spp. and Escherichia coli data and Bayesian microbial risk assessment to examine public health risks in agricultural watersheds under tile drainage management","year":2013,"lang":"en","type":"article","venue":"Water Research","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; Ministry of the Environment, Conservation and Parks; Public Health Agency of Canada","funders":"","keywords":"Tile drainage; Watershed; Campylobacter; Drainage; Environmental science; Fecal coliform; Drainage basin; Water quality; Hydrology (agriculture); Biology; Ecology; Geography; Engineering; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003120354,0.0001587118,0.0001823131,0.0001255684,0.0003032563,0.0004929086,0.0003867888,0.00005765157,0.0008978727],"category_scores_gemma":[0.00001451354,0.000101574,0.00001289979,0.0002654728,0.0001958167,0.0006522243,0.002249844,0.0003140841,0.0001941877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004653571,"about_ca_system_score_gemma":0.00001140921,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02480122,"about_ca_topic_score_gemma":0.002001086,"domain_scores_codex":[0.9969155,0.0008760893,0.0002883142,0.0006366506,0.0004762643,0.0008072024],"domain_scores_gemma":[0.9991213,0.00002203731,0.00003251884,0.0004345863,0.00002507252,0.0003644626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006694406,0.0009565731,0.5974776,0.0003807383,0.000121014,0.00005282457,0.01713341,0.0001624899,0.3220573,0.0004293214,0.009445352,0.05171644],"study_design_scores_gemma":[0.0005535753,0.00008838459,0.990667,0.00001708679,0.000003341534,0.00000289449,0.001225976,0.002519962,0.0007210328,0.0002857137,0.003733868,0.000181174],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896061,0.000007243022,0.0007454142,0.006911489,0.00003707803,0.001046024,0.00002468461,0.00001623001,0.001605772],"genre_scores_gemma":[0.9949439,0.00001644777,0.003471654,0.0004379765,0.00002328189,0.00004260622,0.00009882924,0.00001317547,0.0009520941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3931894,"threshold_uncertainty_score":0.9831076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.208400880869203,"score_gpt":0.3992918227033575,"score_spread":0.1908909418341545,"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."}}