{"id":"W4387603756","doi":"10.1002/aws2.1357","title":"Filter operation effects on plant‐scale microbial risk: Opportunities for enhanced treatment performance","year":2023,"lang":"en","type":"article","venue":"AWWA Water Science","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health; Regional Municipality of Waterloo; BP (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Filter (signal processing); Filtration (mathematics); Scale (ratio); Process (computing); Climate change; Environmental science; Computer science; Risk analysis (engineering); Environmental resource management; Business; Ecology; Mathematics; Statistics; Geography","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":[0.001208274,0.000527413,0.0005274133,0.0003375101,0.000316723,0.001562231,0.0005956409,0.000834194,0.001160503],"category_scores_gemma":[0.001724788,0.0002060679,0.0007014093,0.0003083533,0.0004691491,0.001090729,0.0007317701,0.0006307927,0.0001123953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001501435,"about_ca_system_score_gemma":0.0008492492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008034264,"about_ca_topic_score_gemma":0.0041136,"domain_scores_codex":[0.999586,0.0001131083,0.0000186954,0.00009410088,0.00008221161,0.0001057834],"domain_scores_gemma":[0.9987677,0.000751153,0.0002335069,0.00008834803,0.0001109587,0.00004833042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005659682,0.0004656335,0.01791919,0.0002205099,0.00009797858,0.0001622703,0.0001033454,0.8324366,0.1270551,0.004102523,0.000335476,0.01653549],"study_design_scores_gemma":[0.00002823027,0.001056206,0.01302476,0.00001911406,0.00009006579,0.00005412058,0.0001499462,0.9250808,0.05701209,0.0025185,0.0009070474,0.00005910057],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9462809,0.0003669485,0.04938976,0.0003467432,0.00001921562,0.00005946733,0.0001978676,0.0001336058,0.003205638],"genre_scores_gemma":[0.9974362,0.0001017046,0.002129512,0.00001119956,0.000001492859,0.0000120321,0.00002174622,0.000006179719,0.0002799279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008034264,"threshold_uncertainty_score":0.015975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.043076276561494,"score_gpt":0.252463254877009,"score_spread":0.209386978315515,"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."}}