{"id":"W2333884059","doi":"10.1080/14685248.2013.841319","title":"A-priori evaluations of subgrid-scale terms for large-eddy simulation of compressible turbulent flows","year":2013,"lang":"en","type":"article","venue":"Journal of Turbulence","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mechanics; Turbulence; Compressibility; Large eddy simulation; Turbulence kinetic energy; Physics; Dissipation; Thermodynamics; Statistical physics; Direct numerical simulation; Mach number; Reynolds number","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":[0.0009531625,0.0008333705,0.0005027065,0.0005426293,0.0005066492,0.0008162709,0.0007001611,0.0008345955,0.001261358],"category_scores_gemma":[0.003522673,0.0002872235,0.0005302226,0.0003098457,0.0003832003,0.0005498627,0.0006609153,0.0008017004,0.0002051322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007371774,"about_ca_system_score_gemma":0.000774943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004854192,"about_ca_topic_score_gemma":0.006166758,"domain_scores_codex":[0.9997979,0.00007469894,0.00001902532,0.00001326202,0.00007178884,0.00002336667],"domain_scores_gemma":[0.9982795,0.001011233,0.0001890552,0.0001530512,0.0002623422,0.0001048835],"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":[0.0002369602,0.0001943042,0.004135835,0.0001308428,0.00005059933,0.0001456062,0.00007578413,0.9599257,0.02057571,0.003642718,0.0003192558,0.01056673],"study_design_scores_gemma":[0.00000925836,0.000028077,0.0005547428,0.000003638789,0.000003639726,0.000007157309,0.000005341084,0.9970722,0.002076508,0.0001261587,0.0001086451,0.000004594328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8069698,0.0005865325,0.1836369,0.0002244718,0.00009316002,0.0001912296,0.000447532,0.001145332,0.00670509],"genre_scores_gemma":[0.9446861,0.0001470491,0.05334275,0.0000278531,0.00001749681,0.0001507423,0.0003898618,0.0002253277,0.001012819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004854192,"threshold_uncertainty_score":0.009651899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273361486863046,"score_gpt":0.2643074455512395,"score_spread":0.251573830682609,"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."}}