{"id":"W2002371619","doi":"10.1016/j.jbiomech.2005.03.006","title":"Smooth surface meshing for automated finite element model generation from 3D image data","year":2005,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":96,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"U.S. National Library of Medicine; Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Hexahedron; Smoothing; Polygon mesh; Finite element method; Mesh generation; Voxel; Tetrahedron; Computer science; Algorithm; Quadrilateral; Constraint (computer-aided design); Image (mathematics); Process (computing); Computational science; Computer vision; Mathematics; Geometry; Structural engineering; Engineering; Computer graphics (images)","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":[0.0009369588,0.001084864,0.001088444,0.001402995,0.0005511385,0.001018865,0.001677183,0.00158361,0.003927499],"category_scores_gemma":[0.00395025,0.001421647,0.001376896,0.001019501,0.0008456655,0.0007246993,0.001645114,0.001496767,0.001596973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004489393,"about_ca_system_score_gemma":0.000963567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003312679,"about_ca_topic_score_gemma":0.005278521,"domain_scores_codex":[0.9994327,0.0001093491,0.00005113231,0.00008345555,0.0002829753,0.00004040823],"domain_scores_gemma":[0.9984381,0.000864641,0.00009055508,0.0002512446,0.0003077183,0.00004782879],"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":[0.0002854641,0.0001393003,0.001140234,0.0002866957,0.0001023481,0.0003121479,0.0003856606,0.4697534,0.06598841,0.01007754,0.006641809,0.444887],"study_design_scores_gemma":[0.00001294454,0.00001858183,0.000107794,0.000005155709,0.00000582562,0.00003038576,0.00001315019,0.9907207,0.005645448,0.002416295,0.001014752,0.000008953432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004705696,0.00002790431,0.9923023,0.00002808462,0.00001316347,0.00004969591,0.00007350655,0.002594708,0.0002050761],"genre_scores_gemma":[0.1246929,0.00009345323,0.8720509,0.00005584574,0.00001386747,0.0002791536,0.0006312025,0.001128163,0.001054419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003927499,"threshold_uncertainty_score":0.01313877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04801060773022951,"score_gpt":0.2788434793385381,"score_spread":0.2308328716083086,"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."}}