{"id":"W375298342","doi":"","title":"Estimation of lean mass by bioelectrical impedance analysis : influence of training frequency and modality","year":2002,"lang":"en","type":"book","venue":"Library and Archives Canada (Government of Canada)","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Bioelectrical impedance analysis; Modality (human–computer interaction); Lean body mass; Electrical impedance; Computer science; Engineering; Medicine; Artificial intelligence; Electrical engineering; Internal medicine; Body weight; Body mass index","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005460352,0.0004647457,0.0005405734,0.0006114551,0.0001555556,0.0004672862,0.0005637936,0.0002380496,0.002730998],"category_scores_gemma":[0.002691305,0.0002046257,0.0002556435,0.0008445543,0.0001850797,0.0002572871,0.0002204718,0.0003371006,0.001389752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003393605,"about_ca_system_score_gemma":0.0003943226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0350358,"about_ca_topic_score_gemma":0.09622698,"domain_scores_codex":[0.9996129,0.0000474665,0.0000145995,0.0000446234,0.0002665901,0.00001383099],"domain_scores_gemma":[0.9992083,0.0005019878,0.00004592433,0.00004172467,0.0001835879,0.00001850672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001699903,0.0001272516,0.0552968,0.0003954628,0.0002077577,0.0002427557,0.0003545379,0.004241191,0.04537127,0.000352143,0.01017534,0.8815356],"study_design_scores_gemma":[0.00006285285,0.001131483,0.8757352,0.0003093158,0.0005555477,0.003117865,0.0005797077,0.01979543,0.06517569,0.001379593,0.03205876,0.00009856564],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6649336,0.0910638,0.1573085,0.001974905,0.001132471,0.0002091597,0.005796662,0.00204522,0.07553563],"genre_scores_gemma":[0.8115258,0.02671062,0.05802761,0.0004243275,0.0001680209,0.000125862,0.002251709,0.0005003266,0.1002658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0350358,"threshold_uncertainty_score":0.0696637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006523596306775563,"score_gpt":0.1717011048934539,"score_spread":0.1651775085866783,"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."}}