{"id":"W4366525291","doi":"10.1016/j.compfluid.2023.105897","title":"A superconsistent collocation method for high Reynolds number flows","year":2023,"lang":"en","type":"article","venue":"Computers & Fluids","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Reynolds number; Mathematics; Collocation (remote sensing); Collocation method; Projection method; Projection (relational algebra); Convection–diffusion equation; Mathematical analysis; Vortex; Convergence (economics); Geometry; Physics; Mechanics; Turbulence; Differential equation; Computer science; Algorithm","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":[0.0005356534,0.0004300519,0.0007753123,0.0005869368,0.0005836739,0.0007148226,0.001190567,0.00120427,0.00337193],"category_scores_gemma":[0.001968321,0.0003888208,0.0004487979,0.000673282,0.0006467736,0.0007840897,0.001285834,0.001091211,0.000938813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002697907,"about_ca_system_score_gemma":0.0007385496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002868332,"about_ca_topic_score_gemma":0.003291232,"domain_scores_codex":[0.9996014,0.0001345525,0.00002204687,0.00003884099,0.0001788765,0.00002433376],"domain_scores_gemma":[0.9991485,0.0003248052,0.00003944695,0.0001569727,0.0002679118,0.00006240501],"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":[0.0004490148,0.0002362965,0.001207175,0.0003371203,0.00008857936,0.0003262445,0.0003238649,0.4446021,0.05347559,0.04688061,0.008785258,0.4432881],"study_design_scores_gemma":[0.00001211299,0.00002531086,0.0001003443,0.000006975191,0.000004248417,0.00002057017,0.000007351282,0.99381,0.002042683,0.002041843,0.001917253,0.00001134071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01492687,0.000125082,0.9829128,0.00005673194,0.0001582539,0.0000294352,0.00004230237,0.0003044333,0.001444131],"genre_scores_gemma":[0.2496365,0.0002254009,0.7428379,0.0001081629,0.0001335812,0.0001430183,0.0001839665,0.0003588234,0.006372689],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00337193,"threshold_uncertainty_score":0.01128024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01197935827730232,"score_gpt":0.2426191668814908,"score_spread":0.2306398086041885,"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."}}