{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001991877,0.0008116281,0.0002831028,0.0007697465,0.0001919702,0.0003288293,0.000476458,0.0003370442,0.001129949],"category_scores_gemma":[0.005911428,0.0002418825,0.0005506082,0.000332084,0.000114644,0.000263824,0.0004484502,0.0004389579,0.0005568237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003782706,"about_ca_system_score_gemma":0.0004581301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156458,"about_ca_topic_score_gemma":0.0149354,"domain_scores_codex":[0.9996487,0.0001217372,0.00003716978,0.00008899244,0.00008401884,0.00001939682],"domain_scores_gemma":[0.9988431,0.0005099207,0.000235657,0.0000839528,0.0002885194,0.00003892138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001241401,0.00008793214,0.9666009,0.00003011133,0.0001038113,0.00007464348,0.0001524675,0.004889972,0.001003553,0.00006542312,0.000654052,0.02621304],"study_design_scores_gemma":[0.0000448419,0.0002908962,0.9334269,0.00004198714,0.000198882,0.000236675,0.0001728838,0.0631291,0.001304191,0.0002720262,0.0008670149,0.00001461876],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817524,0.0002305672,0.01590574,0.00009843434,0.00001181135,0.0001068101,0.001200281,0.0000918833,0.0006020486],"genre_scores_gemma":[0.9832429,0.0001472167,0.01468903,0.00002554109,0.000007611367,0.0001155112,0.001245607,0.00001622539,0.0005102059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01156458,"threshold_uncertainty_score":0.02299452,"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."}}