{"id":"W2037551532","doi":"10.1016/j.medengphy.2006.06.010","title":"Establishment of an architecture-specific experimental validation approach for finite element modeling of bone by rapid prototyping and high resolution computed tomography","year":2006,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hexahedron; Finite element method; Solver; Orthotropic material; Smoothing; Strain gauge; Rapid prototyping; Polygon mesh; Tetrahedron; Stiffness; Scale (ratio); Materials science; Tomography; Computer science; Computational science; Algorithm; Structural engineering; Geometry; Mathematics; Engineering; Physics; Composite material; Computer vision; Optics","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":[0.00517389,0.0009148027,0.0005774329,0.0006044985,0.0005681805,0.0008303314,0.001444354,0.001156985,0.001323571],"category_scores_gemma":[0.01006912,0.0006351907,0.0005488968,0.0003367556,0.001055547,0.00102302,0.001234523,0.001074388,0.0003342193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004979667,"about_ca_system_score_gemma":0.0009505927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007873909,"about_ca_topic_score_gemma":0.001168833,"domain_scores_codex":[0.9972789,0.0005816995,0.0002184121,0.0002351713,0.001580285,0.000105523],"domain_scores_gemma":[0.9928357,0.002604179,0.000518527,0.002170393,0.001773661,0.00009757702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001522429,0.0003211945,0.003393073,0.0002998916,0.00004610459,0.0002040011,0.0003317739,0.1219093,0.8018019,0.008806846,0.0003691886,0.06236451],"study_design_scores_gemma":[0.00005990866,0.001271334,0.004868461,0.00006006231,0.00004331377,0.0006513711,0.0001049196,0.3357881,0.6474991,0.002500881,0.007055341,0.00009732279],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05245813,0.00007762059,0.9456491,0.00004370541,0.00003252901,0.0002628494,0.00007219919,0.0003744483,0.001029407],"genre_scores_gemma":[0.3246011,0.0001451006,0.6730608,0.00003171894,0.00001067393,0.0010066,0.000225307,0.0001093194,0.0008094541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00517389,"threshold_uncertainty_score":0.02736247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127047023361587,"score_gpt":0.2209607724108433,"score_spread":0.2096903021772274,"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."}}