{"id":"W2782190440","doi":"10.1016/j.atmosres.2018.01.004","title":"Comparison of three ice cloud optical schemes in climate simulations with community atmospheric model version 5","year":2018,"lang":"en","type":"article","venue":"Atmospheric Research","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Longwave; Shortwave; Cloud forcing; Climate model; Environmental science; Cloud albedo; Climatology; Atmospheric sciences; Cloud feedback; Atmospheric model; Radiative transfer; Parametrization (atmospheric modeling); Radiative flux; Albedo (alchemy); Meteorology; Cloud cover; Cloud computing; Climate change; Climate sensitivity; Geology; Physics; Computer science","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":[],"category_scores_codex":[0.00129614,0.000239669,0.0004191409,7.768932e-7,0.000757476,0.00004568808,0.0008210129,0.000171747,0.001777459],"category_scores_gemma":[0.0001231466,0.0001990883,0.00006126643,0.001820208,0.001940518,0.0002952446,0.0009829863,0.0009551755,0.000372953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003413607,"about_ca_system_score_gemma":0.00008194978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002656566,"about_ca_topic_score_gemma":0.00328278,"domain_scores_codex":[0.9966099,0.0003402912,0.0004976536,0.0004378012,0.001211051,0.000903315],"domain_scores_gemma":[0.998188,0.0004519151,0.0001281023,0.0008924793,0.0001277872,0.0002117842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003987711,0.0008018928,0.8017361,0.00003479959,0.00001892135,0.000004249996,0.001473688,0.1862827,0.002763017,0.0005029238,0.0007729015,0.005210068],"study_design_scores_gemma":[0.0008041547,0.0009972083,0.1149342,0.00006362263,0.00001312141,0.000001859375,0.001578111,0.8789424,0.0008921668,0.0006186027,0.0009021906,0.0002524144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9689717,0.00006136546,0.01469565,0.0001139893,0.00004720737,0.0004243967,0.000004821595,0.0000439929,0.01563689],"genre_scores_gemma":[0.9076309,0.00002189386,0.09193222,0.00003893701,0.0000429197,0.00002171377,0.00000848591,0.0000374256,0.0002655679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6926597,"threshold_uncertainty_score":0.9991351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07786696562988402,"score_gpt":0.3776466404870914,"score_spread":0.2997796748572074,"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."}}