{"id":"W4283662350","doi":"10.2166/wcc.2022.106","title":"Climate change impacts on the flow regime and water quality indicators using an artificial neural network (ANN): a case study in Saskatchewan, Canada","year":2022,"lang":"en","type":"article","venue":"Journal of Water and Climate Change","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin; China Institute of Water Resources and Hydropower Research; Ministry of Agriculture - Saskatchewan","keywords":"Climate change; Environmental science; Downscaling; Precipitation; Streamflow; Water quality; Hydrology (agriculture); Climate change scenario; Representative Concentration Pathways; Range (aeronautics); Climate model; Climatology; Drainage basin; Meteorology; Ecology; Geography; Geology","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":[0.0005913143,0.0005461336,0.0003044301,0.000801739,0.001982661,0.001272729,0.001029032,0.0006655976,0.001044301],"category_scores_gemma":[0.001123626,0.0002504522,0.0005364714,0.002731324,0.0008106596,0.0003805185,0.0005883308,0.0007222821,0.0001196069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02590076,"about_ca_system_score_gemma":0.01950737,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9863382,"about_ca_topic_score_gemma":0.9925412,"domain_scores_codex":[0.9995535,0.0000825899,0.00002659421,0.00005934312,0.0001319297,0.0001461007],"domain_scores_gemma":[0.9990129,0.0002476462,0.00005351421,0.00004503014,0.0005402698,0.000100617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003630361,0.0008075219,0.679722,0.000217106,0.0004589989,0.009624894,0.001067795,0.2516423,0.004616478,0.002302881,0.005802691,0.04337429],"study_design_scores_gemma":[0.0001158783,0.0001657054,0.5031924,0.0001037825,0.0002013082,0.0003597558,0.009179777,0.4770688,0.003130113,0.001007344,0.005329802,0.0001453318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931545,0.0001256779,0.0009238409,0.0003294788,0.00001344193,0.00007324905,0.0007662212,0.00003337868,0.00458011],"genre_scores_gemma":[0.995881,0.0002172682,0.001260792,0.0000804678,0.000003229125,0.00002690251,0.0004849091,0.000007472269,0.002038053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02590076,"threshold_uncertainty_score":0.187924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06746236073296069,"score_gpt":0.2842920771423907,"score_spread":0.21682971640943,"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."}}