{"id":"W2781875450","doi":"10.3390/environments5010008","title":"Riverine Water Quality Response to Precipitation and Its Change","year":2018,"lang":"en","type":"article","venue":"Environments","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precipitation; Turbidity; Environmental science; Climate change; Water quality; Hydrology (agriculture); Chloride; Drainage basin; Ecology; Geology; Geography; Chemistry; Meteorology","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":[0.0001712198,0.0001200722,0.0001523984,0.0002371758,0.0002490719,0.0005603926,0.0001536577,0.000165863,0.0004816267],"category_scores_gemma":[0.0004529098,0.00007121404,0.0001802925,0.0006371606,0.0002922648,0.0001396257,0.0002478957,0.0001795643,0.00005182331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001590116,"about_ca_system_score_gemma":0.001124382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3430019,"about_ca_topic_score_gemma":0.3980809,"domain_scores_codex":[0.999853,0.00001988129,0.000007543496,0.00003115928,0.00005309884,0.00003540926],"domain_scores_gemma":[0.9997349,0.00002866165,0.00007168479,0.00001195679,0.0001209535,0.00003178446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009298329,0.00002380378,0.9806076,0.00002560962,0.0001034253,0.0001228539,0.0001592758,0.003542654,0.008424412,0.0001011733,0.0002075202,0.00658865],"study_design_scores_gemma":[0.000001335382,0.0000137863,0.9981351,0.000001414802,0.000009923011,0.00001444376,0.0001500464,0.0008859966,0.0004553904,0.00002988967,0.0003002476,0.000002446852],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984679,0.00009022012,0.0002468193,0.00005007336,0.000002883478,0.000005502639,0.0002864618,0.000008636078,0.0008415851],"genre_scores_gemma":[0.9995396,0.00005382513,0.00007486405,0.00001057802,0.000001708293,0.00000203449,0.0001562919,0.000001064364,0.0001600164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3430019,"threshold_uncertainty_score":0.6820108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06029825549995693,"score_gpt":0.3228412149888595,"score_spread":0.2625429594889026,"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."}}