{"id":"W2638390972","doi":"10.1299/jsmecmd.2010.23.607","title":"412 Large Scale Parallel Mesh Generation Method for Hierarchical Domain Decomposition Method with Mesh Refinement","year":2010,"lang":"en","type":"article","venue":"Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Domain decomposition methods; Computer science; Mesh generation; Decomposition; Domain (mathematical analysis); Subdivision; Algorithm; Decomposition method (queueing theory); Computational science; Parallel computing; Mathematics; Finite element method; Engineering; Structural engineering; Discrete mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.005574749,0.002078814,0.002151919,0.001521327,0.002208665,0.001864584,0.004382312,0.001165389,0.000237562],"category_scores_gemma":[0.0001432516,0.001905546,0.001088082,0.002432046,0.0004131876,0.00222988,0.001404847,0.002127175,0.00006852249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003624187,"about_ca_system_score_gemma":0.0006849851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002699081,"about_ca_topic_score_gemma":0.001533911,"domain_scores_codex":[0.9863779,0.001500782,0.002675865,0.004166739,0.002318492,0.002960247],"domain_scores_gemma":[0.9906807,0.0009016505,0.001376745,0.004253174,0.001291384,0.00149637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003651188,0.001993948,0.002231452,0.0002481666,0.0005910403,0.00008132299,0.004674543,0.0002800602,0.02520677,0.9218848,0.0246552,0.0177876],"study_design_scores_gemma":[0.01177098,0.003679001,0.006836509,0.0004396184,0.000606524,0.0007287245,0.0004902403,0.6539774,0.03441453,0.08100644,0.2000092,0.006040787],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04491482,0.0001548085,0.9410551,0.00399119,0.001678476,0.003961271,0.0002568352,0.002184219,0.001803314],"genre_scores_gemma":[0.09734201,0.0001130058,0.8928819,0.004176967,0.001573586,0.001693618,0.001143218,0.0003419276,0.0007337072],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8408783,"threshold_uncertainty_score":0.9991953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661717703925705,"score_gpt":0.3280958012555362,"score_spread":0.3114786242162792,"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."}}