{"id":"W4410230478","doi":"10.2166/wh.2025.020","title":"Particulate contaminants and treatment decision-making: maximizing the value of raw water pathogen monitoring for drinking water safety","year":2025,"lang":"en","type":"article","venue":"Journal of Water and Health","topic":"Parasitic Infections and Diagnostics","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canada Research Chairs","keywords":"Cryptosporidium; Environmental science; Raw water; Water quality; Particulates; Water treatment; Contamination; Work (physics); Environmental monitoring; Risk assessment; Environmental engineering; Risk analysis (engineering); Environmental resource management; Environmental planning; Business; Computer science; Engineering; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007673817,0.0001003065,0.0003232007,0.00008361888,0.0004084441,0.00001963113,0.00004838871,0.00006872894,0.000009170011],"category_scores_gemma":[0.00001883949,0.00003979234,0.00007207121,0.00001608413,0.00006007184,0.00005062723,0.00004284618,0.0000962831,0.00000161416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000563172,"about_ca_system_score_gemma":0.00003593259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003695241,"about_ca_topic_score_gemma":0.000009395366,"domain_scores_codex":[0.9990334,0.00008509152,0.0004730969,0.00009381374,0.00002542757,0.0002891931],"domain_scores_gemma":[0.9994877,0.0002387388,0.0000779732,0.00008683925,0.0000862384,0.00002248335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002494306,0.0005714589,0.3878009,0.000306015,0.001326514,0.00004712061,0.0277869,0.0008917593,0.4125831,0.001858416,0.0003417531,0.1639917],"study_design_scores_gemma":[0.005712613,0.001633982,0.116097,0.001095427,0.0003725178,0.0005187202,0.001059121,0.00006100953,0.8453045,0.005294521,0.02265537,0.0001952533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926254,0.001377186,0.003933622,0.001079251,0.0007466348,0.0001985408,0.000004987278,0.000003274293,0.00003116404],"genre_scores_gemma":[0.9984408,0.0009542657,0.0003529743,0.0001161842,0.00004995403,0.000005071495,0.000004319192,0.000005312912,0.00007113425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4327214,"threshold_uncertainty_score":0.3141463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220521713710123,"score_gpt":0.3262523089028692,"score_spread":0.3042001375318569,"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."}}