{"id":"W2003233438","doi":"10.1016/j.watres.2014.01.050","title":"Changes in Escherichia coli to Cryptosporidium ratios for various fecal pollution sources and drinking water intakes","year":2014,"lang":"en","type":"article","venue":"Water Research","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cryptosporidium; Indicator bacteria; Fecal coliform; Sewage; Feces; Wastewater; Surface water; Environmental science; Escherichia coli; Pollution; Environmental chemistry; Sewage treatment; Water quality; Veterinary medicine; Environmental engineering; Biology; Microbiology; Chemistry; Ecology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002703497,0.00009971597,0.0001265565,0.0001073132,0.0002316498,0.0001226322,0.0001668252,0.00007024301,0.0003056881],"category_scores_gemma":[0.00006653072,0.00006264348,0.0000177071,0.0001106174,0.0001368782,0.0001300491,0.0003002368,0.0001596375,0.0002589583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001291803,"about_ca_system_score_gemma":0.000003464542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158932,"about_ca_topic_score_gemma":0.003838769,"domain_scores_codex":[0.9982896,0.0002957313,0.0001569957,0.0003369374,0.000368592,0.0005521253],"domain_scores_gemma":[0.999655,0.00003394821,0.00001003965,0.0001490535,0.00002350928,0.0001284316],"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":[0.0001558373,0.00008196512,0.03693649,0.00004333805,0.000005150333,0.000002656727,0.0165233,0.0001325787,0.9363767,0.0005530294,0.0005051747,0.008683789],"study_design_scores_gemma":[0.0007308547,0.000416477,0.09147891,0.00001781239,0.000002791462,0.000001155394,0.000336943,0.002115565,0.8298095,0.003422518,0.07144268,0.0002247224],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882091,0.000002534009,0.001579553,0.008962237,0.00005504913,0.0005557292,0.000001581587,0.00002210167,0.000612058],"genre_scores_gemma":[0.9972305,0.000001750814,0.0004335779,0.00051549,0.00007202055,0.00014072,0.00001332336,0.000012523,0.001580078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1065671,"threshold_uncertainty_score":0.3347071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04927881787993365,"score_gpt":0.3154501012293222,"score_spread":0.2661712833493886,"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."}}