{"id":"W2460362911","doi":"10.1007/s00382-016-3227-z","title":"Evidence of added value in North American regional climate model hindcast simulations using ever-increasing horizontal resolutions","year":2016,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Marine Environmental Observation Prediction and Response Network","keywords":"Orography; Hindcast; Climatology; Environmental science; Precipitation; Climate model; Orographic lift; Meteorology; Horizontal resolution; Global wind patterns; Diurnal cycle; Climate change; Geology; Geography; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006222971,0.0002235587,0.000330186,0.0001147711,0.0002451435,0.00002184968,0.000306436,0.00007503477,0.00005984014],"category_scores_gemma":[0.0002270155,0.0001960927,0.0001118115,0.0006379083,0.0006567376,0.0006855092,0.0004324222,0.0001457309,0.00002417597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233812,"about_ca_system_score_gemma":0.00005601572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001999673,"about_ca_topic_score_gemma":0.01491883,"domain_scores_codex":[0.9976825,0.0001786228,0.0006098404,0.0005139382,0.0003711744,0.000644],"domain_scores_gemma":[0.9984702,0.0005452488,0.0003019297,0.0005133532,0.00003250512,0.0001367413],"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":[0.0001821794,0.0001358397,0.3024437,0.00002487836,0.000005297775,0.000002759856,0.0001969897,0.6870589,0.008017097,0.001218079,0.000002542396,0.0007118093],"study_design_scores_gemma":[0.0002945473,0.00006105353,0.09921377,0.0002099671,0.00003039052,0.00001102525,0.00008135191,0.8993381,0.0000233426,0.0005056165,0.000002696842,0.0002281005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885509,0.00001415421,0.009966815,0.0002126419,0.00005181979,0.0002674282,0.0006106191,0.00004757383,0.0002780714],"genre_scores_gemma":[0.9872984,0.0003287769,0.01222799,0.0000468121,0.00001614618,0.000008320674,0.00003898186,0.00002877096,0.000005770772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2122793,"threshold_uncertainty_score":0.8325059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05622121115232279,"score_gpt":0.2893437859102453,"score_spread":0.2331225747579225,"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."}}