{"id":"W4379052093","doi":"10.3389/fenvs.2023.1171210","title":"Impacts of climate change on streamflow in the McKenzie Creek watershed in the Great Lakes region","year":2023,"lang":"en","type":"article","venue":"Frontiers in Environmental Science","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Global Water Futures; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Ministère de l’Environnement, de la Protection de la nature et des Parcs","keywords":"Streamflow; Climate change; Environmental science; Precipitation; Watershed; Snowpack; Global warming; Coupled model intercomparison project; Population; Effects of global warming; Climatology; Snowmelt; Hydrology (agriculture); Climate model; Drainage basin; Geography; Snow; Geology; Oceanography; 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":[0.0001997305,0.0001595911,0.0001328883,0.0005249135,0.0009455415,0.0009476583,0.0003253763,0.0002125048,0.0009996673],"category_scores_gemma":[0.0006861708,0.00009502193,0.0002595079,0.001034158,0.0004606841,0.0003360719,0.0004673857,0.0001854396,0.00005556194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01367624,"about_ca_system_score_gemma":0.007790485,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9525654,"about_ca_topic_score_gemma":0.9808092,"domain_scores_codex":[0.9997681,0.00002667409,0.000008251138,0.00004784635,0.0000640749,0.0000850712],"domain_scores_gemma":[0.9997132,0.00003237623,0.0000462703,0.000009334017,0.0001284255,0.00007035094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005812083,0.00002764624,0.9813156,0.00004326115,0.00008471125,0.0003537562,0.0008182461,0.005246005,0.001286952,0.0003936703,0.00160827,0.008763794],"study_design_scores_gemma":[0.000003962329,0.00001061742,0.9940425,0.00001191872,0.00001911576,0.00002528715,0.001152081,0.002595227,0.0001812371,0.00007237227,0.00187582,0.000009889367],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955522,0.0002123947,0.00009337516,0.0002323291,0.000004960676,0.00001446521,0.001071008,0.00001760436,0.002801573],"genre_scores_gemma":[0.9990073,0.0001369315,0.0001285749,0.00003123413,0.000003114735,0.000005995648,0.0002687992,0.00000214416,0.0004159593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04743463,"threshold_uncertainty_score":0.0992285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01690178284842137,"score_gpt":0.2238835743688997,"score_spread":0.2069817915204783,"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."}}