{"id":"W3085113678","doi":"10.7575/aiac.ijkss.v.8n.3p.14","title":"Head, Neck, Trunk, and Pelvis Tissue Mass Predictions for Older Adults using Anthropometric Measures and Dual-Energy X-Ray Absorptiometry","year":2020,"lang":"en","type":"article","venue":"International Journal of Kinesiology and Sports Science","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Trunk; Anthropometry; Pelvis; Lean body mass; Medicine; Dual-energy X-ray absorptiometry; Linear regression; Nuclear medicine; Regression analysis; Population; Soft tissue; Anatomy; Mathematics; Statistics; Surgery; Body weight; Internal medicine; Bone mineral; Biology; Osteoporosis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004704588,0.0001029951,0.000231519,0.0007208902,0.0001353335,0.00003948055,0.0001050559,0.00006598853,0.0000298441],"category_scores_gemma":[0.0003510225,0.00008272214,0.00003124187,0.000401869,0.0005052442,0.0002984929,0.00004383764,0.0001202327,8.844898e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004117108,"about_ca_system_score_gemma":0.0001276253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001408259,"about_ca_topic_score_gemma":0.000001324035,"domain_scores_codex":[0.9987465,0.00001800685,0.000356948,0.0002376658,0.0005080144,0.000132853],"domain_scores_gemma":[0.9985579,0.00005880507,0.0002420995,0.00005648399,0.0008778505,0.0002068207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001750178,0.0003747017,0.2583236,0.0001287792,0.0001828812,0.0005282198,0.0009017107,0.0001425285,0.6893398,0.001305509,0.0008349164,0.04618715],"study_design_scores_gemma":[0.004095398,0.002714449,0.9488121,0.0006900338,0.0001901528,0.005170604,0.0003673884,0.01396414,0.0193998,0.0006950579,0.003615526,0.0002853405],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.945411,0.001043265,0.05024872,0.002751276,0.0003579058,0.0001211093,0.000008541671,0.00001827335,0.00003994553],"genre_scores_gemma":[0.9862041,0.000642587,0.01228924,0.00052897,0.0003101401,0.000002383741,0.0000030028,0.000006743172,0.00001282489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6904885,"threshold_uncertainty_score":0.3373311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03043409743896419,"score_gpt":0.331593060712658,"score_spread":0.3011589632736938,"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."}}