{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003714416,0.0003474953,0.0004124168,0.0003783038,0.0004169585,0.0003662408,0.0007157749,0.0004842302,0.003906192],"category_scores_gemma":[0.0006025907,0.0002386841,0.0006673664,0.0003108487,0.000300398,0.0004445465,0.0007644471,0.000585521,0.001086318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000288705,"about_ca_system_score_gemma":0.0005240553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001565994,"about_ca_topic_score_gemma":0.001870615,"domain_scores_codex":[0.9997073,0.0000648019,0.00001247291,0.00003974035,0.0001575136,0.00001812605],"domain_scores_gemma":[0.9998071,0.00006209849,0.00001401791,0.0000395235,0.00006446618,0.00001283101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001595197,0.00008770442,0.001481111,0.0004505267,0.00008648966,0.0003746845,0.0003680309,0.3325274,0.1021235,0.06722949,0.01170867,0.483403],"study_design_scores_gemma":[0.00002607562,0.00002901368,0.0002067432,0.00001574553,0.00001298877,0.0001264583,0.00002320561,0.9745911,0.007088015,0.005630095,0.01223811,0.00001254289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002839619,0.00007031097,0.9944986,0.0000386646,0.00003853775,0.00003372985,0.00002303095,0.0003707263,0.002086855],"genre_scores_gemma":[0.0993436,0.0001321533,0.8962235,0.00005219425,0.00002296706,0.0001656777,0.000180041,0.0002179789,0.003662012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003906192,"threshold_uncertainty_score":0.01306748,"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."}}