{"id":"W4411915652","doi":"10.5194/gmd-18-3921-2025","title":"Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression","year":2025,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Alliance de recherche numérique du Canada; Korea Polar Research Institute","keywords":"Boundary layer; Gaussian process; Cloud computing; Kriging; Regression; Process (computing); Layer (electronics); Boundary (topology); Gaussian; Statistical physics; Environmental science; Computer science; Statistics; Mathematics; Physics; Machine learning; Materials science; Mechanics; Mathematical analysis; Nanotechnology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006098461,0.000328866,0.0002748527,0.0003546383,0.0001997605,0.0003934844,0.0004061429,0.0004858273,0.0003995085],"category_scores_gemma":[0.002236906,0.0001755463,0.0003420913,0.0003839133,0.0003101873,0.0003495906,0.0002130041,0.0005120382,0.00006640329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006054548,"about_ca_system_score_gemma":0.0004404192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03113761,"about_ca_topic_score_gemma":0.01545141,"domain_scores_codex":[0.9998926,0.00003455384,0.000005788211,0.00002814192,0.00001868019,0.00002010827],"domain_scores_gemma":[0.9992551,0.0004957499,0.00007885556,0.00005053682,0.00008096196,0.00003874386],"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":[0.00003775976,0.00004092549,0.01135085,0.000008960769,0.0000226775,0.00002389392,0.00001952428,0.9841411,0.001386862,0.0003322356,0.0001036125,0.002531708],"study_design_scores_gemma":[0.000001767196,0.000002647135,0.001117075,4.135228e-7,9.854195e-7,7.240752e-7,0.000001253452,0.9986886,0.000139181,0.00003678138,0.000009552213,0.000001017001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843642,0.00002376273,0.0148238,0.0000525507,0.000006292378,0.00001111968,0.0001631455,0.0001704019,0.0003846511],"genre_scores_gemma":[0.9978243,0.000007895527,0.001954885,0.000004494285,0.000001200992,0.000006266532,0.0001310169,0.000009252038,0.00006068801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03113761,"threshold_uncertainty_score":0.06191278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0678718589790969,"score_gpt":0.2951582190742465,"score_spread":0.2272863600951496,"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."}}