{"id":"W2326340394","doi":"10.1016/j.ijheatfluidflow.2016.03.004","title":"Scaling and statistics of large-defect adverse pressure gradient turbulent boundary layers","year":2016,"lang":"en","type":"article","venue":"International Journal of Heat and Fluid Flow","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada; Barcelona Supercomputing Center; Australian Respiratory Council","keywords":"Boundary layer; Turbulence; Laminar sublayer; Mechanics; Reynolds number; Pressure gradient; Adverse pressure gradient; Boundary layer thickness; Physics; Reynolds stress; Turbulence kinetic energy; Direct numerical simulation; Velocity gradient; Scaling; Geometry; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001891968,0.0001099214,0.0001763987,0.0001304908,0.00002828833,0.00002481382,0.0001118957,0.00004513494,0.0000501761],"category_scores_gemma":[0.00003552999,0.00007753383,0.00007320305,0.00002574887,0.00004958798,0.0001460346,0.00003309174,0.00009155109,0.000001463541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003397363,"about_ca_system_score_gemma":0.00002036576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004882317,"about_ca_topic_score_gemma":0.000004320021,"domain_scores_codex":[0.9991497,0.00001503207,0.0003415905,0.00008176155,0.0002819041,0.0001299978],"domain_scores_gemma":[0.9995356,0.00008608583,0.00004466115,0.00006134973,0.0001798057,0.0000925567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00240581,0.001062085,0.06025748,0.001252999,0.01111621,0.001966021,0.006441981,0.3131505,0.1876336,0.03387647,0.05365941,0.3271774],"study_design_scores_gemma":[0.002977268,0.0002197309,0.006761959,0.0005187938,0.0001437039,0.0004703619,0.00004277668,0.9513298,0.001187012,0.001220213,0.0348677,0.000260729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9313506,0.007001596,0.05908874,0.0004181519,0.001557303,0.00007929927,0.0003392236,0.00001924951,0.0001458413],"genre_scores_gemma":[0.9934868,0.002820494,0.00341074,0.000039251,0.0001695321,8.356261e-7,0.000005069077,0.00001587825,0.00005137743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6381793,"threshold_uncertainty_score":0.3161738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004534277670588097,"score_gpt":0.2141392567715037,"score_spread":0.2096049791009157,"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."}}