{"id":"W2089729606","doi":"10.1007/s00382-011-1068-3","title":"Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations","year":2011,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":280,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ouranos; Université du Québec à Montréal","funders":"Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Precipitation; Climatology; Climate model; Nested set model; Environmental science; Orography; Scale (ratio); Orographic lift; Forcing (mathematics); Spatial ecology; Downscaling; Climate change; Meteorology; Geography; Computer science; Geology; Cartography; Data mining","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":[0.0004641141,0.0001831363,0.0001970352,0.00005646853,0.0002194676,0.00003331154,0.0001439993,0.0001817996,0.00005996703],"category_scores_gemma":[0.00003947586,0.0001966276,0.0000535707,0.000212887,0.0001547958,0.0006581229,0.0001490323,0.000108683,0.0000112385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002700095,"about_ca_system_score_gemma":0.00001021192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000935602,"about_ca_topic_score_gemma":0.000590934,"domain_scores_codex":[0.998415,0.00007923619,0.0004292559,0.0004486622,0.0001717515,0.0004560724],"domain_scores_gemma":[0.9993978,0.0001070083,0.000143593,0.00023138,0.00002999009,0.00009027205],"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":[0.0004406132,0.0003891181,0.01068158,0.00007318878,0.00001171487,0.000001613044,0.001121728,0.9526111,0.002306251,0.03177187,0.0001245577,0.0004666969],"study_design_scores_gemma":[0.0008987545,0.00008080058,0.0384566,0.00002341054,0.00003383572,0.000002373037,0.0001212825,0.9295516,0.0000140755,0.0305963,0.00001474352,0.0002062053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966812,0.00001488022,0.03121903,0.0001967369,0.0001194702,0.0007067036,0.000535579,0.00007842886,0.0003171567],"genre_scores_gemma":[0.9876192,0.000225906,0.01046036,0.0001271402,0.00001082167,0.00006301446,0.001438753,0.00002783306,0.00002696579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02777502,"threshold_uncertainty_score":0.8018239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04333311497128128,"score_gpt":0.2422637013331381,"score_spread":0.1989305863618568,"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."}}