{"id":"W4401753022","doi":"10.1103/physrevfluids.9.084608","title":"Robust experimental data assimilation for the Spalart-Allmaras turbulence model","year":2024,"lang":"en","type":"article","venue":"Physical Review Fluids","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Argonne National Laboratory; Office of Science","keywords":"Data assimilation; Turbulence; Kalman filter; Ensemble Kalman filter; Computational fluid dynamics; Meteorology; Reynolds number; Mechanics; Flow (mathematics); Aerospace engineering; Computer science; Environmental science; Physics; Extended Kalman filter; Engineering; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001975888,0.0001854726,0.0002177641,0.00001769574,0.00006518407,0.00008481374,0.0004589488,0.00002601496,0.00002670409],"category_scores_gemma":[0.00003513034,0.00012481,0.0001338135,0.0001522577,0.00002917029,0.0002635334,0.0001143154,0.0001542497,0.00006892014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004674898,"about_ca_system_score_gemma":0.00002150343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000177021,"about_ca_topic_score_gemma":8.660373e-7,"domain_scores_codex":[0.9990693,0.00001167465,0.0002017844,0.0003032938,0.0001993553,0.0002145752],"domain_scores_gemma":[0.9990951,0.0001647521,0.000009203661,0.0006542035,0.00002051251,0.00005621793],"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":[0.000005211939,0.00008689244,0.000002062117,0.003000949,0.0001415877,0.000003200566,0.0001329128,0.8198696,0.007186146,0.04082169,0.1013504,0.02739936],"study_design_scores_gemma":[0.00005649436,0.00001476723,0.0000059905,0.0006312314,0.00009249238,0.000001412359,0.000001365648,0.966666,0.0001691879,0.0004618511,0.03174534,0.000153803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.004998457,0.5824416,0.4033789,0.002530089,0.001318143,0.001810921,0.0004825349,0.0007361663,0.00230319],"genre_scores_gemma":[0.9576999,0.03821734,0.002149196,0.0004206421,0.0006821388,0.0003008825,0.0002710166,0.00008701123,0.0001719121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9527014,"threshold_uncertainty_score":0.5089604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06878191065738824,"score_gpt":0.3151748981065288,"score_spread":0.2463929874491405,"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."}}