{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001637618,0.0001931351,0.0001437054,0.00002791619,0.0006103923,0.0001349576,0.0004274041,0.00006823758,0.003979348],"category_scores_gemma":[0.00005238566,0.0001352969,0.00006027163,0.0002722899,0.0001831327,0.0002114053,0.0006369841,0.000160939,0.006245439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002437063,"about_ca_system_score_gemma":0.0000692629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002336718,"about_ca_topic_score_gemma":0.000264789,"domain_scores_codex":[0.9978389,0.00003790446,0.0003778988,0.0007067753,0.0004883732,0.0005502124],"domain_scores_gemma":[0.9989281,0.0001339995,0.00006497254,0.0007240607,0.00001725921,0.000131665],"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":[0.00004505841,0.001754762,0.1612777,0.0002352595,0.00005532714,0.0004229953,0.04733873,0.6063349,0.09606863,0.001919428,0.0361767,0.04837047],"study_design_scores_gemma":[0.0003559309,0.00001723924,0.003996321,0.00002285016,0.00001377132,0.0003061621,0.0006098762,0.9335209,0.001423472,0.0009421885,0.0582916,0.000499744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9741353,0.000009002923,0.004568364,0.0002588861,0.0004653175,0.0005160719,0.00001970019,0.0000645661,0.0199628],"genre_scores_gemma":[0.9585786,0.000003577822,0.009279982,0.0003222317,0.00001223046,0.00004184971,0.00001821932,0.00001631424,0.03172699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.327186,"threshold_uncertainty_score":0.9969311,"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."}}