{"id":"W2885252519","doi":"10.5194/gmd-12-879-2019","title":"DCMIP2016: the splitting supercell test case","year":2019,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Climate variability and models","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Office of Naval Research; National Nuclear Security Administration; Office of Science; National Aeronautics and Space Administration; Sandia National Laboratories; University of California, Davis; U.S. Department of Energy; University of Colorado Boulder; National Oceanic and Atmospheric Administration; National Center for Atmospheric Research; National Science Foundation","keywords":"Supercell; Core model; Perturbation (astronomy); Statistical physics; Physics; Hydrostatic equilibrium; Convection; Meteorology; Storm; Mechanics; Mathematics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0007953857,0.0005320378,0.0004544641,0.0003972191,0.001115511,0.001039426,0.001715437,0.001303866,0.003601801],"category_scores_gemma":[0.002803177,0.0002069354,0.0006266127,0.0006786233,0.0007595855,0.001006488,0.0007917706,0.000959256,0.0004141572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375255,"about_ca_system_score_gemma":0.001612185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04602576,"about_ca_topic_score_gemma":0.02297069,"domain_scores_codex":[0.9995345,0.000123268,0.00001853992,0.00007127956,0.0001093308,0.0001430222],"domain_scores_gemma":[0.9987397,0.0004728731,0.00007256864,0.0002078081,0.0003033531,0.0002036915],"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":[0.0004409647,0.0003700117,0.01254779,0.00009745418,0.00006075145,0.0004414831,0.0001149587,0.9530841,0.002324849,0.01340646,0.01014211,0.006969085],"study_design_scores_gemma":[0.0002585796,0.0001448247,0.002713663,0.00001316198,0.00001759596,0.00006915571,0.0001578668,0.9848969,0.003623985,0.003938563,0.004141604,0.00002422365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9443679,0.0001632839,0.01310802,0.0009808111,0.0001842331,0.0002565149,0.006336592,0.0004731414,0.03412948],"genre_scores_gemma":[0.9894427,0.00004756334,0.005849794,0.0001032729,0.00002152714,0.00009716699,0.003032811,0.0000635627,0.001341562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04602576,"threshold_uncertainty_score":0.09151572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01734536480810965,"score_gpt":0.2095009629490873,"score_spread":0.1921555981409777,"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."}}