{"id":"W4413471057","doi":"10.2166/hydro.2025.009","title":"The application of data-driven modelling for the water quality index: a case study in Canada","year":2025,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Science Foundation of Anhui Province","keywords":"Index (typography); Environmental science; Quality (philosophy); Water quality; Computer science; Statistics; Data mining; Mathematics; Physics; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001904073,0.0005187819,0.0004259252,0.0009634868,0.001353033,0.001638653,0.001290981,0.0008047601,0.0006846854],"category_scores_gemma":[0.004651802,0.0002753364,0.0007836937,0.002316468,0.0007700854,0.0005022285,0.0006386915,0.0009079627,0.00006755989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02140502,"about_ca_system_score_gemma":0.014437,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9433799,"about_ca_topic_score_gemma":0.9234553,"domain_scores_codex":[0.9992197,0.0002852036,0.00004149887,0.0001155874,0.0002107675,0.0001274052],"domain_scores_gemma":[0.9975193,0.001217256,0.0001144191,0.0001236171,0.0009189985,0.0001064703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001662698,0.0003060241,0.08186926,0.0001285335,0.0001427135,0.000915952,0.0003860421,0.8871936,0.001258197,0.004425514,0.001728162,0.02147976],"study_design_scores_gemma":[0.00002442184,0.0000423946,0.02079553,0.00001341649,0.00002796554,0.00002930034,0.0005532566,0.9754484,0.0009357911,0.0008201179,0.001277966,0.00003147151],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820701,0.0001305079,0.0121082,0.0007026406,0.00001411682,0.0001691242,0.001284185,0.0001247872,0.003396273],"genre_scores_gemma":[0.988492,0.00008712152,0.009781241,0.00004428712,0.00000312923,0.00004613348,0.0005503425,0.00001427699,0.0009813625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05662006,"threshold_uncertainty_score":0.155305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05895403423187281,"score_gpt":0.3199619047247628,"score_spread":0.26100787049289,"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."}}