{"id":"W3090696194","doi":"10.1175/mwr-d-20-0020.1","title":"A Convection Parameterization for Low-CAPE Environments","year":2020,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Convective available potential energy; Convection; Free convective layer; Convective inhibition; Diurnal cycle; Squall line; Environmental science; Precipitation; Atmospheric convection; Parametrization (atmospheric modeling); Climatology; Atmospheric sciences; Meteorology; Geology; Mechanics; Combined forced and natural convection; Physics; Natural convection; Radiative transfer","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001229899,0.00008682739,0.0001831511,0.000008698172,0.00007159355,0.00001598553,0.00008351444,0.00003058981,0.003318806],"category_scores_gemma":[0.00009262258,0.00006354491,0.0000741313,0.00009713079,0.00001862685,0.00008634268,0.000003781744,0.00004050784,0.0003968706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002127604,"about_ca_system_score_gemma":0.000004842161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001023633,"about_ca_topic_score_gemma":0.00000526654,"domain_scores_codex":[0.9993256,0.00006613293,0.0001968366,0.0001901265,0.00009618195,0.0001250876],"domain_scores_gemma":[0.9996662,0.00007168589,0.00006565049,0.0000916533,0.000006718351,0.00009815125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002570979,0.0001742032,0.09033427,0.004179418,0.0001853037,0.000009326009,0.0007838951,0.04689407,0.001168901,0.0006579104,0.007727532,0.8476281],"study_design_scores_gemma":[0.0005323176,0.0005694555,0.04470053,0.0002420509,0.0001196654,5.581313e-7,0.00001225359,0.1248458,0.00003936982,0.001390923,0.8272367,0.0003103557],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.3683461,0.4576088,0.0812479,0.02778494,0.001218302,0.01363477,0.001140123,0.0005414315,0.04847765],"genre_scores_gemma":[0.9770392,0.007213936,0.001362625,0.01352143,0.0001289107,0.00002932139,0.0004616842,0.000006568072,0.0002363532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8473177,"threshold_uncertainty_score":0.9975923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04358318041705942,"score_gpt":0.2366296940324673,"score_spread":0.1930465136154079,"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."}}