{"id":"W3021221633","doi":"10.1016/j.scitotenv.2020.139062","title":"How does climate variability affect water quality dynamics in Canada's oil sands region?","year":2020,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Precipitation; Streamflow; Snow; Watershed; Hydrology (agriculture); Water quality; Seasonality; Spatial variability; Physical geography; Geography; Ecology; Geology; Drainage basin; 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":[0.0005623249,0.0001305312,0.0002494065,0.0007819567,0.001790096,0.002059687,0.0007011623,0.0007389167,0.001555915],"category_scores_gemma":[0.002591236,0.0001921417,0.0004001834,0.001958058,0.001321623,0.000760618,0.0006888526,0.0007649299,0.0001231044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01739453,"about_ca_system_score_gemma":0.02366334,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9914445,"about_ca_topic_score_gemma":0.996343,"domain_scores_codex":[0.9996182,0.00004169429,0.00001855484,0.00006397652,0.00007037082,0.0001870598],"domain_scores_gemma":[0.9983194,0.0002663033,0.0002574212,0.00005133964,0.0007213706,0.0003841355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006913483,0.00004389501,0.9873855,0.00002278664,0.0001217847,0.0001669648,0.001135809,0.001702997,0.0006126972,0.000987755,0.002195698,0.005555049],"study_design_scores_gemma":[0.00000356028,0.000004346732,0.9948884,0.00001143444,0.00002570734,0.00001601479,0.00227799,0.001008408,0.0000887468,0.0001345103,0.001527306,0.00001363785],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905868,0.0004144211,0.00009076075,0.004133146,0.00002572553,0.000006583263,0.001401043,0.00001071403,0.003330809],"genre_scores_gemma":[0.9988041,0.0002428813,0.00003903431,0.0001537894,0.000008887837,0.000001386457,0.0002448077,0.000005782692,0.0004992215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01739453,"threshold_uncertainty_score":0.1262067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008973196995759223,"score_gpt":0.1957641888909627,"score_spread":0.1867909918952035,"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."}}