{"id":"W4393375638","doi":"10.1115/1.4065216","title":"Predicting Tibia-Fibula Geometry and Density From Anatomical Landmarks Via Statistical Appearance Model: Influence of Errors on Finite Element-Calculated Bone Strain","year":2024,"lang":"en","type":"article","venue":"Journal of Biomechanical Engineering","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Fibula; Tibia; Finite element method; Strain (injury); Geometry; Anatomy; Mathematics; Orthodontics; Structural engineering; Medicine; 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":[0.0006456054,0.0001691118,0.0004455489,0.0003648719,0.00002122587,0.00002459899,0.00008025907,0.0001510333,0.00001123129],"category_scores_gemma":[0.0003077833,0.000147473,0.000094325,0.0002938203,0.00003503784,0.0001097134,0.00004750366,0.0006209932,0.00000112606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095059,"about_ca_system_score_gemma":0.00005188385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001042964,"about_ca_topic_score_gemma":7.507155e-7,"domain_scores_codex":[0.9983144,0.00003039579,0.0006895441,0.0002066341,0.0005526533,0.0002064098],"domain_scores_gemma":[0.9992003,0.0001800189,0.0001291005,0.0001355871,0.000133269,0.0002216937],"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":[0.0002908722,0.000106699,0.0006993535,0.0002857959,0.0002161095,0.0002720616,0.00004512497,0.03049844,0.9648238,0.0005347342,0.00002585303,0.002201123],"study_design_scores_gemma":[0.0006595741,0.0004971435,0.00473108,0.001883538,0.0001432734,0.0001092952,0.000007089707,0.9327452,0.05860763,0.0004796954,0.00001146675,0.0001249979],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5881522,0.0001908084,0.4113176,0.0001039814,0.0000520358,0.00008259726,0.00003776481,0.00006078254,0.000002292447],"genre_scores_gemma":[0.976009,0.00003168471,0.02377386,0.00005911949,0.00008622729,0.000001282817,0.00001110606,0.00002628936,0.000001426742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9062162,"threshold_uncertainty_score":0.6013773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065250731131564,"score_gpt":0.2553960025733944,"score_spread":0.2447434952620788,"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."}}