{"id":"W2068026782","doi":"10.2514/6.2005-5333","title":"Parallel Implicit Adaptive Mesh Refinement Scheme for Body-Fitted Multi-Block Mesh","year":2005,"lang":"en","type":"article","venue":"17th AIAA Computational Fluid Dynamics Conference","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Innovation Trust","keywords":"Adaptive mesh refinement; Computer science; Mesh generation; Block (permutation group theory); Scheme (mathematics); Polygon mesh; Parallel computing; Algorithm; Computational science; Mathematics; Computer graphics (images); Finite element method; Geometry; Physics","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":[0.0004175322,0.0004187438,0.0007593858,0.0004064268,0.0004076916,0.0004408527,0.001491448,0.0006428325,0.005043526],"category_scores_gemma":[0.0008509998,0.0002655241,0.0006427967,0.0004910761,0.0002968241,0.0004644228,0.0009795537,0.0008442706,0.001996927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004341468,"about_ca_system_score_gemma":0.0008390852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002442789,"about_ca_topic_score_gemma":0.002612066,"domain_scores_codex":[0.999604,0.00008592325,0.00001847867,0.0000386766,0.000221922,0.0000310394],"domain_scores_gemma":[0.9997484,0.00005997885,0.00002522791,0.00006088319,0.00008975062,0.00001568644],"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":[0.0001122047,0.00006730338,0.0007865843,0.0002295135,0.00005569566,0.0002403177,0.0001471954,0.6846805,0.03965593,0.04516213,0.00805886,0.2208038],"study_design_scores_gemma":[0.00001354149,0.00001964829,0.00009266587,0.000007714283,0.00000406866,0.0000471766,0.000005534991,0.9865596,0.002117403,0.001712238,0.009413197,0.000007242414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003116836,0.0001293853,0.9925379,0.00004522293,0.00005523172,0.00006340705,0.00006780303,0.0006390717,0.003345138],"genre_scores_gemma":[0.0876303,0.0001833777,0.9037948,0.00006058272,0.00002648183,0.0004122325,0.0004535662,0.0002498088,0.00718887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005043526,"threshold_uncertainty_score":0.01687229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01835091850388748,"score_gpt":0.2479331116994964,"score_spread":0.229582193195609,"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."}}