{"id":"W1987153231","doi":"10.1016/j.compfluid.2010.05.006","title":"A hybrid algorithm for far-field noise minimization","year":2010,"lang":"en","type":"article","venue":"Computers & Fluids","topic":"Aerodynamics and Acoustics in Jet Flows","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Solver; Discretization; Airfoil; Noise (video); Algorithm; Computation; Computer science; Field (mathematics); Computational fluid dynamics; Mathematics; Applied mathematics; Mathematical optimization; Mathematical analysis; Physics; Mechanics","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":[0.0006505161,0.0008688138,0.0008308751,0.0006525867,0.0005289919,0.0008778266,0.001569728,0.001729192,0.006638773],"category_scores_gemma":[0.001350011,0.0005625919,0.0007658165,0.00064109,0.0005089974,0.001053002,0.001638259,0.000997493,0.002255609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003320353,"about_ca_system_score_gemma":0.0007222054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002204618,"about_ca_topic_score_gemma":0.00318046,"domain_scores_codex":[0.9996226,0.00008057925,0.00001867176,0.00005967322,0.0001876828,0.00003072587],"domain_scores_gemma":[0.9994677,0.000239278,0.00002582658,0.00005827758,0.0001723305,0.00003668755],"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":[0.0003248092,0.000152338,0.000440075,0.0001386655,0.0001256113,0.0000990512,0.00008865904,0.5224947,0.0208618,0.02502261,0.00541874,0.424833],"study_design_scores_gemma":[0.00002945809,0.00003614564,0.00005164674,0.000004166931,0.000007420336,0.00002396721,0.000004797147,0.9941624,0.001401046,0.00264659,0.001623861,0.000008534201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002027165,0.00005140033,0.9966299,0.00003507138,0.00004397162,0.00001583942,0.00001712529,0.0002490604,0.0009304963],"genre_scores_gemma":[0.06430731,0.00008510664,0.9270382,0.0001179027,0.00007997331,0.0001926663,0.0001699157,0.0002558114,0.007753124],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006638773,"threshold_uncertainty_score":0.02220887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004710819581091345,"score_gpt":0.2038357375072828,"score_spread":0.1991249179261915,"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."}}