{"id":"W3164798129","doi":"10.1016/j.watres.2021.117296","title":"Using surrogate data to assess risks associated with microbial peak events in source water at drinking water treatment plants","year":2021,"lang":"en","type":"article","venue":"Water Research","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Water treatment; Filtration (mathematics); Cryptosporidium; Fecal coliform; Environmental engineering; Indicator bacteria; Biosolids; Clostridium perfringens; Surface water; Indicator organism; Environmental chemistry; Water quality; Pulp and paper industry; Chemistry; Microbiology; Biology; Ecology; Feces; Bacteria","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":[0.003969192,0.0005238356,0.0004213971,0.0008304473,0.0002764052,0.001356253,0.0004316568,0.001037964,0.0005223898],"category_scores_gemma":[0.0166765,0.0002246034,0.000770534,0.0009679925,0.0003579418,0.0007836448,0.0008591179,0.0007990454,0.0001738152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006575906,"about_ca_system_score_gemma":0.0008055396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001588454,"about_ca_topic_score_gemma":0.001790327,"domain_scores_codex":[0.9963332,0.002149018,0.0001848926,0.0003869139,0.0007909417,0.0001549793],"domain_scores_gemma":[0.9877315,0.006396655,0.003869174,0.0009188117,0.0008602761,0.0002236453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003661121,0.0008135085,0.8809613,0.0003403416,0.0005778126,0.0003367109,0.0002632471,0.04774831,0.02373528,0.002444823,0.0008814687,0.03823595],"study_design_scores_gemma":[0.0003120454,0.009690104,0.5379348,0.0002440037,0.0006901914,0.001014634,0.001152143,0.3451668,0.08064034,0.01814087,0.004871928,0.0001422204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9596859,0.0003705421,0.03609975,0.000171748,0.00003838722,0.0001096069,0.001626198,0.00007080734,0.001826989],"genre_scores_gemma":[0.994561,0.0001163679,0.004118199,0.00005257352,0.000008216168,0.00005142648,0.0008406297,0.000008462986,0.0002431371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003969192,"threshold_uncertainty_score":0.02099133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4166097104509122,"score_gpt":0.4259425101071201,"score_spread":0.009332799656207924,"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."}}