{"id":"W2020618342","doi":"10.1016/j.jbiomech.2014.11.042","title":"Comparison of explicit finite element and mechanical simulation of the proximal femur during dynamic drop-tower testing","year":2014,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Stiffness; Mean squared error; Finite element method; Femur; Bone mineral; Tower; Drop (telecommunication); Correlation coefficient; Quantitative computed tomography; Biomechanics; Structural engineering; Materials science; Orthodontics; Mechanics; Biomedical engineering; Mathematics; Composite material; Statistics; Engineering; Physics; Anatomy; Surgery; Osteoporosis; Medicine; Mechanical 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":[0.001007747,0.00009610895,0.0004285424,0.0001782881,0.00007335553,0.000008124751,0.0001108573,0.00009151135,0.00001656345],"category_scores_gemma":[0.0009120146,0.00006349129,0.00009053171,0.0003127652,0.00002622675,0.00005591899,0.00009374836,0.000327187,4.883152e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007204909,"about_ca_system_score_gemma":0.0001229839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008162786,"about_ca_topic_score_gemma":0.000004729633,"domain_scores_codex":[0.998116,0.00008630458,0.0008686458,0.0001067805,0.0006112167,0.0002110335],"domain_scores_gemma":[0.9981605,0.0002993104,0.0007605914,0.0001845116,0.0004461589,0.000148865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006368252,0.0003938643,0.005987315,0.000805382,0.00005189225,0.000003322674,0.0003148626,0.0004052011,0.9501719,0.00009341317,0.00001345935,0.04112251],"study_design_scores_gemma":[0.001947377,0.001941669,0.006227821,0.0005797258,0.00007231885,0.00004453422,0.0002634555,0.8612664,0.1271725,0.0003048236,0.0001133613,0.00006607604],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908714,0.0002018244,0.008025081,0.0004715821,0.0001179284,0.0002792063,0.000003396638,0.000004522382,0.00002501026],"genre_scores_gemma":[0.9976921,0.00002934396,0.002134453,0.00004620581,0.00005805364,0.000001569362,9.000673e-7,0.00001305787,0.00002431852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8608612,"threshold_uncertainty_score":0.25891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04661752512981177,"score_gpt":0.371537346038752,"score_spread":0.3249198209089403,"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."}}