{"id":"W2045202040","doi":"10.1175/jas-d-14-0124.1","title":"Evaluating the Diurnal Cycle of Upper-Tropospheric Ice Clouds in Climate Models Using SMILES Observations","year":2014,"lang":"en","type":"article","venue":"Journal of the Atmospheric Sciences","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Diurnal cycle; Empirical orthogonal functions; Environmental science; Climatology; Atmospheric sciences; Troposphere; Diurnal temperature variation; Middle latitudes; Climate model; Amplitude; Annual cycle; Geology; Climate change; Oceanography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003432942,0.0001709588,0.000300718,9.040246e-7,0.0006685683,0.0000759057,0.00154053,0.0000529008,0.0002570538],"category_scores_gemma":[0.0002634655,0.00008807875,0.0002060214,0.001325896,0.0009574954,0.0006145448,0.0004129665,0.0002717211,0.000003378621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001610936,"about_ca_system_score_gemma":0.00009907178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007121125,"about_ca_topic_score_gemma":0.00006283963,"domain_scores_codex":[0.9970602,0.0003661235,0.0007939087,0.0002202993,0.001142571,0.0004168916],"domain_scores_gemma":[0.9981523,0.0002964033,0.001101634,0.000323029,0.00005014489,0.00007644916],"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":[0.0000158497,0.00005047621,0.1764264,0.000003802895,0.00000676841,4.460908e-7,0.0005052587,0.8135245,0.006415481,0.0003172028,0.00003640489,0.002697307],"study_design_scores_gemma":[0.0002465725,0.0002331535,0.141958,0.0000760355,0.0000354539,0.00003690762,0.001111599,0.850084,0.0001550944,0.005880732,0.00007122631,0.0001111869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938941,0.0002591723,0.003328546,0.0008085987,0.0004553356,0.0001336113,6.962113e-7,0.000005507548,0.00111446],"genre_scores_gemma":[0.9347516,0.00007311433,0.06467034,0.0003455625,0.00009226069,0.000002199327,3.738797e-8,0.00001120668,0.00005370008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06134179,"threshold_uncertainty_score":0.5142155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06477399157033066,"score_gpt":0.3155262131242595,"score_spread":0.2507522215539289,"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."}}