{"id":"W2253697112","doi":"10.1123/jab.21.4.371","title":"Predicting in Vivo Soft Tissue Masses of the Lower Extremity Using Segment Anthropometric Measures and DXA","year":2005,"lang":"en","type":"article","venue":"Journal of Applied Biomechanics","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Waterloo; University of Windsor; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Anthropometry; Thigh; Linear regression; Soft tissue; Mathematics; Regression analysis; Fat mass; Lean body mass; Stepwise regression; Medicine; Bone mineral content; Nuclear medicine; Body mass index; Statistics; Anatomy; Orthodontics; Body weight; Surgery; Bone density; Internal medicine","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":[0.0010412,0.0001385563,0.0003553745,0.000531835,0.0000610797,0.00001736135,0.0001387468,0.0001091439,0.0000375165],"category_scores_gemma":[0.00006968656,0.00009674459,0.00007215802,0.0007739292,0.00006073338,0.00008811936,0.00007654014,0.0002859593,2.807874e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002575496,"about_ca_system_score_gemma":0.0001139877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001014882,"about_ca_topic_score_gemma":0.000007308998,"domain_scores_codex":[0.9982879,0.00003318298,0.0006314213,0.0001324881,0.0007443785,0.0001706597],"domain_scores_gemma":[0.9989153,0.00004424579,0.0005565695,0.0001841773,0.0002178638,0.00008181661],"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":[0.000167102,0.0004158285,0.001942999,0.00005983204,0.00004515993,0.000007414211,0.0001007531,0.00002862109,0.9817276,0.00007980025,0.0001278454,0.01529704],"study_design_scores_gemma":[0.001048541,0.0003257987,0.000419367,0.0004192722,0.0001357494,0.0001791917,0.0001737116,0.002224867,0.9933151,0.0003722551,0.001285665,0.0001005091],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786954,0.0006856701,0.01923514,0.0004418195,0.0001843433,0.0004105288,0.00000469407,0.00002405888,0.000318355],"genre_scores_gemma":[0.9751539,0.0001816168,0.02435882,0.0001121737,0.0001690484,0.00000167828,2.015223e-7,0.00001687461,0.000005721356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01519653,"threshold_uncertainty_score":0.394513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962604727578592,"score_gpt":0.2887071405569548,"score_spread":0.2590810932811689,"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."}}