{"id":"W4394722596","doi":"10.5194/egusphere-2024-352","title":"Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Korea Polar Research Institute; Ministry of Oceans and Fisheries","keywords":"Boundary layer; Cloud computing; Gaussian process; Boundary (topology); Regression; Kriging; Process (computing); Statistical physics; Econometrics; Gaussian; Layer (electronics); Environmental science; Statistics; Computer science; Mathematics; Mechanics; Materials science; Physics; Mathematical analysis; Nanotechnology","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":[0.0006776855,0.0004081021,0.0002859369,0.0004336623,0.0002173311,0.0004458557,0.0003853001,0.0005446248,0.0003998846],"category_scores_gemma":[0.00208443,0.0001642628,0.000405837,0.0004365339,0.0003256171,0.0003566023,0.0002136134,0.0004698358,0.00007672177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004885142,"about_ca_system_score_gemma":0.0003351941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02708511,"about_ca_topic_score_gemma":0.01193476,"domain_scores_codex":[0.9998602,0.0000418986,0.000006817318,0.00003793542,0.00002696163,0.00002627216],"domain_scores_gemma":[0.9992864,0.0004694724,0.00007794452,0.00005324659,0.00007478864,0.00003824514],"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":[0.00008652345,0.00007213555,0.02105303,0.00001766531,0.00004242887,0.00005539069,0.00003822776,0.970035,0.003556242,0.0004377308,0.0002060168,0.004399712],"study_design_scores_gemma":[0.000003079221,0.000004224826,0.002425395,6.983089e-7,0.000001466155,0.000001483636,0.000002496768,0.9971813,0.0003148241,0.00004666648,0.00001638944,0.000001910348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855885,0.00002748483,0.0134996,0.00004256239,0.000007615451,0.00001155065,0.000214869,0.0002571643,0.0003506196],"genre_scores_gemma":[0.9977747,0.000009294698,0.001924058,0.000005137452,0.000001531555,0.000006074524,0.0001990376,0.00001337707,0.00006677966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02708511,"threshold_uncertainty_score":0.05385494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08517365384161943,"score_gpt":0.3516736033885371,"score_spread":0.2664999495469177,"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."}}