{"id":"W2936911327","doi":"10.1093/femsle/fnz067","title":"Detection of fecal bacteria and antibiotic resistance genes in drinking water collected from three First Nations communities in Manitoba, Canada","year":2019,"lang":"en","type":"article","venue":"FEMS Microbiology Letters","topic":"Pharmaceutical and Antibiotic Environmental Impacts","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Collaborative Research Based on Industrial Demand; Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Fecal coliform; Water quality; Antibiotic resistance; Biology; Feces; Water treatment; Microbiology; Bacteria; Antibiotics; Environmental science; Ecology; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003459704,0.0007055683,0.0003395074,0.00240564,0.004304325,0.001402319,0.0008180217,0.0004788593,0.0009297507],"category_scores_gemma":[0.001029912,0.0003819673,0.0002531993,0.003810517,0.001139511,0.0002109832,0.0009169442,0.00037905,0.0002076474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01380086,"about_ca_system_score_gemma":0.01615753,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9796311,"about_ca_topic_score_gemma":0.9930737,"domain_scores_codex":[0.9993103,0.00005094819,0.00003280315,0.0001119802,0.0002855827,0.0002084724],"domain_scores_gemma":[0.9985307,0.00007885967,0.0001256978,0.00002744474,0.0009925999,0.0002446439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001661744,0.0001110746,0.9408206,0.000151805,0.00005047619,0.0007403363,0.01097727,0.0001855396,0.03090317,0.0001248533,0.0006691886,0.01509946],"study_design_scores_gemma":[0.000009481999,0.00009431427,0.982806,0.00003560868,0.00003131264,0.0002701298,0.01089662,0.0001999362,0.002859471,0.00002207692,0.002751945,0.00002304727],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966375,0.0003290859,0.0002299975,0.0001104923,0.000006639165,0.00009737488,0.00092355,0.00001060475,0.001654719],"genre_scores_gemma":[0.9912372,0.0006965386,0.001875086,0.0002560102,0.000006033042,0.00007279359,0.00172225,0.000009811711,0.004124445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02036887,"threshold_uncertainty_score":0.1001327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138224817766647,"score_gpt":0.1906543490080597,"score_spread":0.1792721008303932,"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."}}