{"id":"W2331914037","doi":"10.2514/6.2013-1000","title":"A Polynomial Adaptive LCP Scheme for Viscous Compressible Flows","year":2013,"lang":"en","type":"article","venue":"51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Scheme (mathematics); Polynomial; Compressibility; Computer science; Applied mathematics; Mathematical optimization; Mathematics; Mechanics; Mathematical analysis; Physics","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":[0.000199787,0.0002190821,0.0004023372,0.0002222057,0.0003080117,0.0004633808,0.0007783775,0.0004830541,0.002925449],"category_scores_gemma":[0.0009170281,0.0001427974,0.0001931616,0.0003893898,0.0004327604,0.0004756897,0.0009720928,0.000769949,0.0005680905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004271546,"about_ca_system_score_gemma":0.0005604391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004750197,"about_ca_topic_score_gemma":0.003634301,"domain_scores_codex":[0.9998679,0.00002983808,0.000006509437,0.00001608771,0.00006380081,0.00001586125],"domain_scores_gemma":[0.9997888,0.00004928193,0.00001508433,0.00005056815,0.00007462229,0.00002165492],"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":[0.0005700634,0.0001986811,0.0009450436,0.0002330356,0.00002299315,0.0001837368,0.0001578349,0.5010828,0.05620743,0.07356406,0.006616017,0.3602182],"study_design_scores_gemma":[0.00001526354,0.00001920482,0.0000641321,0.000002268203,0.000001523921,0.000008120084,0.000003360471,0.996402,0.001188153,0.001177774,0.001114574,0.000003498278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05564169,0.000373367,0.9326771,0.0003048593,0.0002310177,0.00008886231,0.0001145563,0.0007671468,0.009801436],"genre_scores_gemma":[0.7335545,0.0002498607,0.2548584,0.0001213255,0.0001163591,0.0001260987,0.0001361183,0.0001884927,0.01064889],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004750197,"threshold_uncertainty_score":0.009786606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588558740525265,"score_gpt":0.2400658916721364,"score_spread":0.2241803042668838,"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."}}