{"id":"W4413276328","doi":"10.1016/j.ijheatfluidflow.2025.110004","title":"Upstream history quantification and scale-decomposed energy analysis for weak-to-strong adverse-pressure-gradient turbulent boundary layers","year":2025,"lang":"en","type":"article","venue":"International Journal of Heat and Fluid Flow","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"H2020 European Research Council; European Research Council; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; University of Melbourne; Istanbul Teknik Üniversitesi; Partnership for Advanced Computing in Europe AISBL","keywords":"Turbulence; Adverse pressure gradient; Scale (ratio); Pressure gradient; Upstream (networking); Mechanics; Boundary (topology); Materials science; Scale analysis (mathematics); Flow separation; Physics; Mathematical analysis; Mathematics; Computer science","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":[0.0007185805,0.0005515238,0.0003277783,0.00177308,0.0003256137,0.001181294,0.0005703086,0.0005260437,0.001257145],"category_scores_gemma":[0.002081584,0.0002501278,0.0005034721,0.000837092,0.000332072,0.00101114,0.0008365951,0.0005550256,0.000254684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000284477,"about_ca_system_score_gemma":0.0003626369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0027669,"about_ca_topic_score_gemma":0.002686771,"domain_scores_codex":[0.9998004,0.00002713544,0.0000220431,0.00004904708,0.00006877381,0.0000326657],"domain_scores_gemma":[0.9994911,0.0001569582,0.000118292,0.00009129554,0.0001070521,0.00003530605],"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":[0.0008462894,0.0005056539,0.1520898,0.0009479484,0.0003114875,0.001215742,0.00109603,0.4071584,0.1435673,0.01580012,0.002904139,0.2735572],"study_design_scores_gemma":[0.00002697476,0.00007076766,0.07591656,0.00006863251,0.00007076186,0.0001285364,0.0003568704,0.8924518,0.02450928,0.003697775,0.002615812,0.00008616133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7948442,0.0007215797,0.198888,0.0001282937,0.00006068888,0.00007428761,0.001035752,0.0008636812,0.003383628],"genre_scores_gemma":[0.9699543,0.0002239599,0.02815194,0.00002860136,0.00001968265,0.00003558346,0.0009188589,0.0001506527,0.0005163953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0027669,"threshold_uncertainty_score":0.005501628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007087396180570305,"score_gpt":0.2214550549371384,"score_spread":0.2143676587565681,"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."}}