{"id":"W4389576872","doi":"10.1109/embc40787.2023.10340324","title":"Cross-day analysis of Multicode Surface Electromyography based Biometrics for Personal Identification","year":2023,"lang":"en","type":"article","venue":"","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biometrics; Computer science; Gesture; Leverage (statistics); Electromyography; Speech recognition; Identification (biology); Artificial intelligence; Pattern recognition (psychology); Physical medicine and rehabilitation; Medicine","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":[0.001488725,0.0004173033,0.0005210057,0.0009251891,0.000261287,0.0005400924,0.0002697076,0.000433054,0.001016342],"category_scores_gemma":[0.003346801,0.0001079356,0.0003868244,0.0006938315,0.0002458643,0.0004725931,0.0008269079,0.0003233486,0.0004171765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001685669,"about_ca_system_score_gemma":0.0002164642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001001477,"about_ca_topic_score_gemma":0.003023188,"domain_scores_codex":[0.9986871,0.0003124111,0.00007416488,0.0003274662,0.0004868096,0.000112046],"domain_scores_gemma":[0.9979792,0.0006306396,0.0002095277,0.0002588469,0.0008053348,0.000116553],"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":[0.002244284,0.0007286061,0.2168984,0.0006236912,0.0007997588,0.0004696686,0.002158696,0.0104803,0.2774099,0.0007485533,0.001588653,0.4858494],"study_design_scores_gemma":[0.00001505726,0.001468323,0.9085683,0.00003722144,0.0001688441,0.0009857538,0.0009670813,0.04914592,0.0361178,0.0005374811,0.001906627,0.00008150149],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9660306,0.0003100287,0.03197592,0.00003591072,0.00004780332,0.00007871423,0.0002423263,0.00008845438,0.001190191],"genre_scores_gemma":[0.9877536,0.00009958214,0.01115125,0.00001745511,0.00001822023,0.00004879057,0.0002727998,0.00001765218,0.00062066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001488725,"threshold_uncertainty_score":0.007873237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02157081791578073,"score_gpt":0.2816936831597359,"score_spread":0.2601228652439552,"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."}}