{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002058214,0.0001832845,0.0002873917,0.00007968382,0.0008651106,0.00004532536,0.0001409988,0.00003100459,0.0001670114],"category_scores_gemma":[0.00000306903,0.00009648941,0.00003361441,0.0001104736,0.0001040008,0.0002906858,0.0006284405,0.0003167588,0.000001266985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001441024,"about_ca_system_score_gemma":0.000004430493,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0714224,"about_ca_topic_score_gemma":0.4194258,"domain_scores_codex":[0.9979801,0.0004928993,0.0003885819,0.0002286048,0.0003030241,0.0006067587],"domain_scores_gemma":[0.9995584,0.00003361061,0.0001403298,0.0001458827,0.000005369946,0.0001164406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008199104,0.000573341,0.7934062,0.00005090794,0.00009144277,0.008176632,0.1882811,0.002514454,0.000431549,0.000004876267,0.00022398,0.005425551],"study_design_scores_gemma":[0.004047916,0.005138577,0.5253899,0.0001015207,0.0005003812,0.003887597,0.4498287,0.006941485,0.001124906,0.0005694326,0.001058524,0.001411115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968709,0.00005350487,2.264601e-7,0.002358925,0.0002723604,0.000394251,0.00001796823,0.000005344126,0.00002653391],"genre_scores_gemma":[0.997929,0.00005295541,0.000006154477,0.00177242,0.0001853919,0.00003295434,0.000003950497,0.0000129749,0.000004135684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3480034,"threshold_uncertainty_score":0.9347611,"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."}}