{"id":"W4206142825","doi":"10.1002/essoar.10509963.1","title":"Multi-campaign analysis of smoke properties and cloud interactions in the Southeast Atlantic using ORACLES, LASIC, and CLARIFY data with WRF-CAM5","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Allison University","funders":"Nuclear Safety and Security Commission; National Aeronautics and Space Administration; U.S. Department of Energy","keywords":"Weather Research and Forecasting Model; Aerosol; Environmental science; Atmospheric sciences; Cloud computing; Meteorology; Shortwave; Climate model; Climatology; Radiative transfer; Climate change; Geography; Computer science; Geology; 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":[0.0004922694,0.0004358038,0.0002773471,0.0005156123,0.0003684532,0.0003808362,0.0004402679,0.0003782081,0.0007446794],"category_scores_gemma":[0.00055497,0.0001701154,0.0006621798,0.0004219353,0.0001966715,0.0004643627,0.0002756531,0.0004204003,0.0001493534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007960428,"about_ca_system_score_gemma":0.0005964121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1626484,"about_ca_topic_score_gemma":0.1922653,"domain_scores_codex":[0.9998419,0.00001855165,0.00001202412,0.00004081564,0.00004392527,0.00004260328],"domain_scores_gemma":[0.9996827,0.00005861097,0.00004784728,0.00005072429,0.0001094415,0.00005081599],"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":[0.0004818566,0.0006039304,0.9168636,0.00008096532,0.0002476528,0.0002710575,0.0002472582,0.03840099,0.02103412,0.0004850753,0.003049237,0.01823428],"study_design_scores_gemma":[0.0001108527,0.0001444285,0.870536,0.00001795703,0.0001029161,0.00003701122,0.0002709039,0.1208207,0.005772093,0.0001104004,0.002042982,0.00003387276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968283,0.00003308138,0.0003460892,0.00006391441,0.00001233558,0.00001555032,0.001727305,0.0001255568,0.0008479121],"genre_scores_gemma":[0.9942055,0.00002369241,0.0007880736,0.00004466523,0.00001182099,0.00001283281,0.004668728,0.00002006882,0.0002245769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1626484,"threshold_uncertainty_score":0.3234033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1362269605061469,"score_gpt":0.2835847975295883,"score_spread":0.1473578370234413,"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."}}