{"id":"W3109071147","doi":"10.3390/rs12223825","title":"Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; University of Maryland","keywords":"Visible Infrared Imaging Radiometer Suite; Radiance; Radiative transfer; Environmental science; Remote sensing; Sky; Numerical weather prediction; Atmospheric radiative transfer codes; Brightness temperature; Zenith; Meteorology; Computer science; Climatology; Satellite; Geology; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003748085,0.0001697536,0.0001941902,0.00002203834,0.0006727784,0.000054488,0.0000723931,0.00005971351,0.000001734071],"category_scores_gemma":[0.00008079461,0.0001635894,0.00003929157,0.000152837,0.00007150574,0.0001234283,0.00006032794,0.0005274278,0.000002370399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001412812,"about_ca_system_score_gemma":0.00001334873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001027823,"about_ca_topic_score_gemma":0.00003981639,"domain_scores_codex":[0.9988777,0.0001488881,0.0001837787,0.0002911721,0.0002128284,0.0002856056],"domain_scores_gemma":[0.9993674,0.0002457184,0.00004872875,0.0001284036,0.00001464836,0.0001951511],"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":[0.00009203795,0.000003393382,0.00006074248,0.00002288424,0.00000683461,6.259779e-7,0.01056638,0.6977736,0.001831344,0.000003139136,0.000001207669,0.2896378],"study_design_scores_gemma":[0.0004563614,0.0001603372,0.0001038524,0.00008407414,0.00002133689,0.000002713,0.001230496,0.9968068,0.0003508026,0.0002023455,0.0003668271,0.0002140777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3906778,0.000006352106,0.6082721,0.0002463073,0.00001042345,0.000383107,0.000001965612,0.00005087197,0.0003510204],"genre_scores_gemma":[0.7653648,0.000002624823,0.234201,0.0003283823,0.00005203565,4.579935e-7,0.000005323383,0.00002685175,0.00001854688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.374687,"threshold_uncertainty_score":0.6670984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05701476881523911,"score_gpt":0.2946135684069877,"score_spread":0.2375987995917486,"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."}}