{"id":"W4289745709","doi":"10.1016/j.cam.2022.114656","title":"A novel Large Eddy Simulation model for the Quasi-Geostrophic equations in a Finite Volume setting","year":2022,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Science Foundation; European Research Council; Radcliffe Institute for Advanced Study, Harvard University; William and Flora Hewlett Foundation; H2020 European Research Council; CANDU Owners Group","keywords":"Ocean gyre; Barotropic fluid; Polygon mesh; Benchmark (surveying); Nonlinear system; Mathematics; Finite volume method; Filter (signal processing); Applied mathematics; Large eddy simulation; Forcing (mathematics); Computer simulation; Data assimilation; Mathematical optimization; Mathematical analysis; Computer science; Mechanics; Meteorology; Geology; Geometry; Physics; Turbulence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003816183,0.0005932426,0.000940247,0.0002250013,0.0005161898,0.0009243403,0.001662533,0.001552384,0.001800787],"category_scores_gemma":[0.0008332422,0.0003310914,0.0006613974,0.0002491962,0.0005998448,0.0009032773,0.001250376,0.001037694,0.0003665054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004798314,"about_ca_system_score_gemma":0.001154813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005774703,"about_ca_topic_score_gemma":0.00594212,"domain_scores_codex":[0.9998444,0.00004935513,0.00001041384,0.00002918294,0.00005149388,0.00001510308],"domain_scores_gemma":[0.9997103,0.0001082929,0.00003654966,0.00002448036,0.00006660064,0.00005368139],"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":[0.00006402243,0.00009227428,0.0004952427,0.00004201553,0.00003504782,0.0001126596,0.00003544338,0.968745,0.007165381,0.01635269,0.0006543759,0.006205865],"study_design_scores_gemma":[0.000007515898,0.000007041637,0.00002107875,8.420686e-7,0.000001707575,0.000003999222,8.824466e-7,0.999134,0.0001183348,0.0004559053,0.0002466048,0.000002170559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04304718,0.000262411,0.9498017,0.0003854208,0.0002488201,0.0000974258,0.0002006868,0.0006700241,0.005286371],"genre_scores_gemma":[0.6881808,0.0003409785,0.2985699,0.0002241166,0.0002257334,0.0004121308,0.0004654446,0.00033547,0.01124536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005774703,"threshold_uncertainty_score":0.01148218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04567781212341821,"score_gpt":0.2612166296924922,"score_spread":0.215538817569074,"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."}}