{"id":"W7114903316","doi":"10.21227/qjm4-zz88","title":"\"Digital Phenotyping of Neuromuscular\\u2013Cognitive Aging Using Portable Ultrasound and Multidomain \"","year":2025,"lang":"","type":"dataset","venue":"IEEE DataPort","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sarcopenia; Benchmark (surveying); Cognition; Anthropometry; Healthy aging; Feature (linguistics); Feature selection; Informed consent","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002537764,0.002859704,0.003829885,0.002295653,0.0009812093,0.001390034,0.002288038,0.001196165,0.0003993805],"category_scores_gemma":[0.003332413,0.003512108,0.0006770906,0.002658204,0.002126015,0.004584997,0.001843497,0.002650756,0.0002620427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004965656,"about_ca_system_score_gemma":0.002546108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001999944,"about_ca_topic_score_gemma":0.0001847557,"domain_scores_codex":[0.9860837,0.00057357,0.004080884,0.004411209,0.002269538,0.002581137],"domain_scores_gemma":[0.9869683,0.001872324,0.004239576,0.004852,0.001196614,0.0008712081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007014209,0.002140621,0.006222176,0.006951581,0.004918905,0.002823732,0.0003835165,0.0007355567,0.04057062,0.00001708815,0.9304758,0.004058947],"study_design_scores_gemma":[0.009718635,0.0003757407,0.002084488,0.01861528,0.01508169,0.003573844,0.003004896,0.007297258,0.008982504,0.0001822612,0.921878,0.009205398],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03858226,0.001544449,0.001901673,0.000008126943,0.002202044,0.002529545,0.952583,0.0001288308,0.0005200388],"genre_scores_gemma":[0.02499548,0.0008995198,0.001182301,0.0001868827,0.0006473688,0.00004803127,0.9713365,0.0003794811,0.0003243974],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03158811,"threshold_uncertainty_score":0.9996502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02151777269151728,"score_gpt":0.2910522420017175,"score_spread":0.2695344693102002,"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."}}