{"id":"W2907032940","doi":"10.1029/2018jd029596","title":"Cloud‐Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties","year":2019,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Office of Science; Israel Science Foundation; National Natural Science Foundation of China; National Key Research and Development Program of China; U.S. Department of Energy","keywords":"Squall line; Precipitation; Atmospheric sciences; Environmental science; Precipitation types; Liquid water content; Graupel; Altitude (triangle); Climatology; Convection; Cloud physics; Mass flux; Forcing (mathematics); Ice crystals; Lapse rate; Meteorology; Geology; Cloud computing; Physics","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":[0.001556174,0.001205281,0.0009461449,0.0006005954,0.0007006159,0.0009539288,0.001610951,0.001491936,0.001906577],"category_scores_gemma":[0.001739297,0.0004375623,0.001328356,0.0008470454,0.0004874381,0.0009029786,0.0005470304,0.000882426,0.0003280897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050937,"about_ca_system_score_gemma":0.0006456453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05745852,"about_ca_topic_score_gemma":0.02477667,"domain_scores_codex":[0.9996086,0.0001488432,0.00002788729,0.00009124655,0.0000504843,0.00007298861],"domain_scores_gemma":[0.9989247,0.0004062528,0.00009448377,0.0001856987,0.0002548565,0.0001341405],"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":[0.00119483,0.0007599043,0.045829,0.00008198267,0.0004800496,0.0003190516,0.0001307556,0.9286259,0.0122422,0.0007795208,0.001867297,0.007689469],"study_design_scores_gemma":[0.0004616905,0.0003782816,0.02252441,0.00000858824,0.00008247436,0.00003160005,0.00007422788,0.9691581,0.006331821,0.0002119864,0.0006940522,0.00004269416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952213,0.0000708002,0.001085627,0.0001490693,0.0000313994,0.00003796654,0.001175956,0.0002474033,0.001980331],"genre_scores_gemma":[0.9953626,0.00002449058,0.00229011,0.0000765391,0.00001533476,0.00003719188,0.001756644,0.0000622017,0.0003748221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05745852,"threshold_uncertainty_score":0.1142482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1019132678413181,"score_gpt":0.3321626569262041,"score_spread":0.230249389084886,"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."}}