{"id":"W3093581344","doi":"10.1111/risa.13612","title":"Impact of Hydrometeorological Events for the Selection of Parametric Models for Protozoan Pathogens in Drinking‐Water Sources","year":2020,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Fecal contamination and water quality","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrometeorology; Environmental science; Cryptosporidium; Snowmelt; Sampling (signal processing); Poisson distribution; Hydrology (agriculture); Atmospheric sciences; Statistics; Ecology; Mathematics; Biology; Precipitation; Meteorology; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01082218,0.0005452769,0.0003798724,0.0004119946,0.00049568,0.0009795079,0.0008933333,0.0005674182,0.0006442192],"category_scores_gemma":[0.01990856,0.0003421168,0.0007331755,0.0001810763,0.000522027,0.0005193235,0.0007727904,0.0008577442,0.0000750462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001385627,"about_ca_system_score_gemma":0.001722675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05786091,"about_ca_topic_score_gemma":0.03475805,"domain_scores_codex":[0.9978912,0.001379356,0.000107063,0.0002837681,0.0001945645,0.0001440173],"domain_scores_gemma":[0.9767585,0.0197133,0.001271821,0.0008076807,0.001125815,0.0003227992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000399371,0.0001323288,0.08995923,0.0000388789,0.0002386742,0.0001177563,0.00006349498,0.8953204,0.002533129,0.0009071747,0.000255714,0.01003382],"study_design_scores_gemma":[0.00002140246,0.00008619862,0.01643531,0.00000873775,0.00002842678,0.00001731408,0.00002797104,0.9812485,0.001609636,0.0003495034,0.0001507533,0.00001615485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436033,0.0001351318,0.05472245,0.0001847693,0.00001721411,0.00009453181,0.0003582842,0.0001546819,0.0007297343],"genre_scores_gemma":[0.9958106,0.0000160463,0.0037924,0.00002089977,0.000002699914,0.0000296556,0.000202216,0.00001150868,0.0001139838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05786091,"threshold_uncertainty_score":0.1150482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03428631124204959,"score_gpt":0.2865251414140457,"score_spread":0.2522388301719961,"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."}}