{"id":"W2411356278","doi":"10.1080/07055900.2016.1185005","title":"Evaluation of Precipitation Indices over North America from Various Configurations of Regional Climate Models","year":2016,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Climate variability and models","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Environment and Climate Change Canada; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; National Aeronautics and Space Administration; U.S. Department of Energy","keywords":"Precipitation; Climatology; Climate model; Environmental science; General Circulation Model; Scale (ratio); Meteorology; Geography; Climate change; Cartography; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.003076033,0.001701177,0.000748285,0.001054251,0.00105834,0.001321643,0.002065757,0.0006407138,0.000774792],"category_scores_gemma":[0.006223477,0.0006436835,0.0009597272,0.001629583,0.0004883413,0.0009102105,0.0007329576,0.0008286641,0.0001469951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008326938,"about_ca_system_score_gemma":0.006721713,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8075603,"about_ca_topic_score_gemma":0.8051506,"domain_scores_codex":[0.9987838,0.0003223996,0.00009464881,0.0002570816,0.0003352536,0.0002068168],"domain_scores_gemma":[0.9971392,0.0007582749,0.0001883915,0.0002460249,0.001423174,0.000244866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005202499,0.0001669265,0.06271942,0.0001274382,0.0005688773,0.0001199738,0.000140186,0.9180621,0.002055134,0.0004770006,0.001637854,0.0134048],"study_design_scores_gemma":[0.0004495482,0.0002953887,0.06609386,0.00004039174,0.0004127382,0.00006152746,0.0002415276,0.9245498,0.004648803,0.0002454292,0.002841431,0.0001195335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864695,0.0003384729,0.002437653,0.0001918325,0.00005315527,0.0001308507,0.003471516,0.0006459526,0.00626108],"genre_scores_gemma":[0.9884704,0.0002310628,0.005868186,0.00005183016,0.00001204481,0.00006030929,0.004622522,0.0001078697,0.0005757056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8075603,"threshold_uncertainty_score":0.3871459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03076342073486305,"score_gpt":0.2596671666094049,"score_spread":0.2289037458745419,"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."}}