{"id":"W4283657225","doi":"10.2166/ws.2022.245","title":"Water quality analysis using the CCME-WQI method with time series analysis in a water supply reservoir","year":2022,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Hydrographic Service","funders":"","keywords":"Water quality; Autoregressive integrated moving average; Environmental science; Inflow; Water supply; Hydrology (agriculture); Time series; Quality (philosophy); Environmental engineering; Statistics; Meteorology; Mathematics; Geology; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001289299,0.0004379172,0.0005156521,0.001271706,0.000379901,0.0006295026,0.0005002539,0.0005181266,0.0006736879],"category_scores_gemma":[0.001920596,0.0002157288,0.000942264,0.00168182,0.000183396,0.0006068633,0.0005275251,0.0004739939,0.00008376072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000826464,"about_ca_system_score_gemma":0.0009711462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03251039,"about_ca_topic_score_gemma":0.01969573,"domain_scores_codex":[0.9994084,0.0001736508,0.00005079943,0.0001142593,0.0001868832,0.00006608557],"domain_scores_gemma":[0.9993048,0.0002610114,0.0000912585,0.00005012088,0.0002517101,0.00004106699],"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":[0.0004453948,0.0009312715,0.2072737,0.0003478718,0.0004921282,0.0006253708,0.0003395466,0.6311268,0.03438585,0.001848672,0.001869888,0.1203135],"study_design_scores_gemma":[0.000006125025,0.0000456999,0.02725042,0.000002870361,0.0000167575,0.00001756566,0.0000555562,0.9705869,0.001725921,0.0001474627,0.0001277256,0.00001697305],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241509,0.00008767809,0.07371355,0.0001292535,0.0000252885,0.00005898679,0.0004805296,0.000241145,0.001112551],"genre_scores_gemma":[0.991359,0.00002715556,0.008067195,0.000007532547,0.000003929577,0.00002784045,0.000233005,0.000008121032,0.0002662178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03251039,"threshold_uncertainty_score":0.06464231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891606299505132,"score_gpt":0.2973941134348866,"score_spread":0.2784780504398353,"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."}}