{"id":"W1950522065","doi":"10.1111/j.1752-1688.2011.00592.x","title":"Modeling Climate Change Impacts on Hydrology and Nutrient Loading in the Upper Assiniboine Catchment<sup>1</sup>","year":2011,"lang":"en","type":"article","venue":"JAWRA Journal of the American Water Resources Association","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Victoria","funders":"Agriculture and Agri-Food Canada","keywords":"Snowmelt; Environmental science; Surface runoff; Hydrology (agriculture); Soil and Water Assessment Tool; Watershed; Climate change; Precipitation; Drainage basin; Hydrological modelling; Nutrient; Streamflow; Climatology; Ecology; Geology; Meteorology; Geography","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":[0.001582351,0.0001296961,0.0002353234,0.00009057115,0.0002476722,0.00002800901,0.000299002,0.00003954382,0.00002832754],"category_scores_gemma":[0.0000474328,0.00006182894,0.00007917276,0.000154897,0.0001284003,0.000182773,0.0002255824,0.000275479,0.00002364565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002262104,"about_ca_system_score_gemma":0.000001137008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005591415,"about_ca_topic_score_gemma":0.00003338735,"domain_scores_codex":[0.9984397,0.0003976305,0.000308813,0.0001451144,0.0003229004,0.0003858506],"domain_scores_gemma":[0.9993485,0.00006300507,0.0004013593,0.0001388897,0.000009629864,0.00003863731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002262685,0.000128754,0.9150437,0.000004914428,0.00008192415,0.00001347356,0.0728616,0.0103272,0.0001930661,0.00001136349,0.0002225249,0.0008851826],"study_design_scores_gemma":[0.001823868,0.001427897,0.9483919,0.000112072,0.0002787853,0.00005625133,0.007380582,0.03226287,0.001124298,0.002444251,0.00429301,0.0004042728],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909943,0.00003407495,0.0000152607,0.007917487,0.00005602965,0.0001563429,0.000001324109,0.000005381899,0.0008198312],"genre_scores_gemma":[0.9966243,0.0002306434,0.00004301746,0.00297121,0.00008246298,0.000008277371,5.256018e-7,0.000008432224,0.00003112746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06548101,"threshold_uncertainty_score":0.2521311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818774078561643,"score_gpt":0.2302052882733765,"score_spread":0.2120175474877601,"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."}}