{"id":"W3042867287","doi":"10.33540/131","title":"Advancing Microbial Risk Assessments of Subsurface Water Sources","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sampling (signal processing); Sample (material); Water quality; Selection (genetic algorithm); Environmental science; Quality (philosophy); Sampling design; Computer science; Biochemical engineering; Data mining; Statistics; Ecology; Engineering; Biology; Mathematics; Machine learning; Environmental health; Population","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002178518,0.000387439,0.0003321866,0.001334185,0.0009767937,0.002982565,0.0004387299,0.0005404879,0.01768233],"category_scores_gemma":[0.004654408,0.0002823322,0.000325488,0.001183886,0.000433277,0.001147942,0.001457604,0.001206338,0.002009453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004598152,"about_ca_system_score_gemma":0.01369611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2864034,"about_ca_topic_score_gemma":0.4932937,"domain_scores_codex":[0.9989285,0.0001952519,0.00002533478,0.00007348683,0.000681526,0.0000958492],"domain_scores_gemma":[0.9976463,0.0004831076,0.0001378085,0.0001113895,0.001419268,0.0002021456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001401277,0.0001696175,0.01849068,0.0005687035,0.00006002793,0.0001261554,0.002009454,0.006296584,0.02006995,0.03111893,0.1731943,0.7477554],"study_design_scores_gemma":[0.00003027668,0.0001524952,0.02847948,0.0007105859,0.00009058388,0.000069711,0.002271357,0.01218243,0.02531377,0.03150386,0.8991421,0.00005344174],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1255005,0.04189439,0.137412,0.1152506,0.001835492,0.0008590457,0.01531705,0.001104046,0.5608268],"genre_scores_gemma":[0.4069195,0.05580992,0.2338695,0.002229362,0.0005085623,0.0003518474,0.00413788,0.0004646437,0.2957089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2864034,"threshold_uncertainty_score":0.5694727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00898541506310017,"score_gpt":0.2702242005071083,"score_spread":0.2612387854440081,"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."}}