{"id":"W2555019917","doi":"10.1080/16864360.2016.1257183","title":"Towards quantitative mesh pre-optimization for finite element analysis","year":2016,"lang":"en","type":"article","venue":"Computer-Aided Design and Applications","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"National Science and Technology Major Project; Wuhan Institute of Technology; Nanjing University of Aeronautics and Astronautics; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Machining; Kinematics; Machine tool; Computer science; Metric (unit); Numerical control; Finite element method; Computation; Bottleneck; Displacement (psychology); Algorithm; Mechanical engineering; Engineering; Structural engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001095882,0.0001339783,0.0002222705,0.0001733285,0.00008603119,0.00002952625,0.0001205173,0.0000425363,0.00002121587],"category_scores_gemma":[0.00001659496,0.000103476,0.00009331373,0.0005777542,0.00003820379,0.0001134873,0.00002740608,0.0000316778,0.000004135371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004325276,"about_ca_system_score_gemma":0.000007301894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001754064,"about_ca_topic_score_gemma":9.465372e-7,"domain_scores_codex":[0.999247,0.00002498491,0.0002487543,0.0002483109,0.00007874328,0.0001522147],"domain_scores_gemma":[0.9990878,0.0004623035,0.00005653829,0.0002293615,0.00009314456,0.00007084916],"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":[0.000008285625,0.00003286877,0.00001819065,0.00001623763,0.0003241107,1.016862e-7,0.00003830999,0.8423401,0.00111255,0.01075384,0.0007261043,0.1446293],"study_design_scores_gemma":[0.0001662966,0.00006701621,0.00008076973,0.000008665917,0.0002188828,1.843616e-7,0.000003671432,0.9841667,0.003160457,0.0068655,0.005103115,0.0001586929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000615487,0.0001043935,0.9984994,0.0001736347,0.000009882145,0.0006712788,0.00002707989,0.0004060816,0.00004672083],"genre_scores_gemma":[0.0722236,0.0002681954,0.9261174,0.00006849219,0.00004434293,0.001182198,0.000038264,0.00001906942,0.00003849413],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1444706,"threshold_uncertainty_score":0.4219629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02137053630805004,"score_gpt":0.2822600138918839,"score_spread":0.2608894775838339,"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."}}