{"id":"W3203435361","doi":"10.20944/preprints202109.0438.v1","title":"Statistical Analysis of Dynamic Subgrid Modeling Approaches in Large Eddy Simulation","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Turbulence; Vortex; Energy cascade; Cascade; Statistical physics; Large eddy simulation; Kinetic energy; Flow (mathematics); Physics; Mechanics; Scale (ratio); Turbulence kinetic energy; Vorticity; Tensor (intrinsic definition); Classical mechanics; Mathematics; Geometry; Engineering","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":[0.002055252,0.0003652216,0.0003879962,0.0008275459,0.0003664425,0.001024484,0.0006659492,0.0003897796,0.0005797567],"category_scores_gemma":[0.004987761,0.0002951113,0.0005068985,0.0005858897,0.000576016,0.001176514,0.0007488221,0.0007637488,0.0001011413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006880675,"about_ca_system_score_gemma":0.0008313349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004167249,"about_ca_topic_score_gemma":0.002581505,"domain_scores_codex":[0.999442,0.0002344165,0.00003640517,0.00007165853,0.0001663604,0.00004909941],"domain_scores_gemma":[0.9971856,0.001728126,0.000309884,0.0003492158,0.0003388214,0.0000882462],"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":[0.00006781112,0.00007764062,0.007486801,0.00003201799,0.00007486083,0.00004835776,0.00007833594,0.9316085,0.003318508,0.04344048,0.0002395852,0.01352706],"study_design_scores_gemma":[0.000001423991,0.000005916939,0.0004009317,9.383409e-7,0.000002398945,0.000001796803,0.000005297843,0.9961051,0.0002645762,0.003095997,0.0001132958,0.000002409014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2879176,0.0002203872,0.7087882,0.0002300893,0.00003587594,0.00005688231,0.000191842,0.0006664346,0.001892795],"genre_scores_gemma":[0.9456441,0.0001444745,0.05328172,0.00003656677,0.00002844658,0.0001077108,0.0001916918,0.0001356422,0.0004296867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004167249,"threshold_uncertainty_score":0.01086938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09011321633786937,"score_gpt":0.3139369498459414,"score_spread":0.2238237335080721,"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."}}