{"id":"W2120654387","doi":"10.1002/joc.3769","title":"Future convective environments using <scp>NARCCAP</scp>","year":2013,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Climate variability and models","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Office of Research and Development; National Center for Atmospheric Research; U.S. Department of Energy; U.S. Environmental Protection Agency; National Aeronautics and Space Administration","keywords":"Convective available potential energy; Climatology; Convection; Convective storm detection; Environmental science; Storm; Wind shear; Severe weather; Population; Meteorology; Convective inhibition; Atmospheric sciences; Geography; Wind speed; Geology; Demography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002561937,0.0001237956,0.0002242183,0.00008057622,0.00005118628,0.000035217,0.0004798672,0.0001161177,0.002504296],"category_scores_gemma":[0.0001307346,0.0001065188,0.000120871,0.00005468526,0.0002138281,0.0005232263,0.0002118665,0.0002391553,0.0005547472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002430409,"about_ca_system_score_gemma":0.00001851324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007330711,"about_ca_topic_score_gemma":0.000008725737,"domain_scores_codex":[0.9985901,0.00009782331,0.0004780094,0.0001684669,0.0004358199,0.0002297826],"domain_scores_gemma":[0.9990945,0.0002112042,0.0004137552,0.0001173385,0.00004667488,0.0001164792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007480803,0.0009859431,0.8177377,0.00001300946,0.0005119669,0.0002874793,0.00334869,0.01495617,0.1453712,0.003719523,0.009780722,0.003212737],"study_design_scores_gemma":[0.008273247,0.000954409,0.4813492,0.0001606987,0.0002950528,0.01430315,0.006870832,0.05753161,0.02814615,0.09729443,0.3041195,0.0007017555],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899594,0.00004475349,0.003382019,0.001170698,0.001393051,0.0001002638,0.000007487034,0.000005096417,0.003937216],"genre_scores_gemma":[0.9963724,0.0001027379,0.002469632,0.0006754855,0.0002371483,0.000003504063,0.000003285607,0.00001147077,0.0001243171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3363885,"threshold_uncertainty_score":0.9984075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01553707090110393,"score_gpt":0.266345421761692,"score_spread":0.250808350860588,"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."}}