{"id":"W4310668502","doi":"10.1139/apnm-2022-0301","title":"Smartwatch-based bioimpedance analysis for body composition estimation: precision and agreement with a 4-compartment model","year":2022,"lang":"en","type":"article","venue":"Applied Physiology Nutrition and Metabolism","topic":"Body Composition Measurement Techniques","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Smartwatch; Wearable computer; Equivalence (formal languages); Computer science; Body water; Medicine; Statistics; Body weight; Internal medicine; Mathematics; Embedded system","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":[0.01485534,0.0008787895,0.001175064,0.001665475,0.0004792543,0.001905763,0.00114262,0.00105585,0.0009350575],"category_scores_gemma":[0.03821753,0.000536015,0.001342719,0.001241555,0.0006733589,0.00101699,0.001434168,0.000673983,0.000677973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005756964,"about_ca_system_score_gemma":0.0007859673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004058879,"about_ca_topic_score_gemma":0.004640416,"domain_scores_codex":[0.9908097,0.004714231,0.0006654823,0.001492132,0.002166103,0.0001523835],"domain_scores_gemma":[0.9794474,0.01334388,0.001954189,0.002594315,0.00253874,0.0001216193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006124384,0.0004172662,0.3772081,0.001667295,0.003932458,0.0003384012,0.002493615,0.108807,0.06525661,0.004698535,0.002475639,0.4265807],"study_design_scores_gemma":[0.0001748116,0.001987649,0.2699055,0.000461729,0.0008806108,0.001107838,0.0004766767,0.6861643,0.0246697,0.006361129,0.007520782,0.0002893036],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2677912,0.001991183,0.7260872,0.0001562628,0.0001458629,0.0003049143,0.0005497107,0.0008748878,0.002098871],"genre_scores_gemma":[0.7904682,0.000559187,0.2066299,0.0001397989,0.00004837013,0.0004744918,0.00065143,0.00017678,0.0008518529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01485534,"threshold_uncertainty_score":0.07856351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02266246274866434,"score_gpt":0.2770902219908451,"score_spread":0.2544277592421808,"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."}}