{"id":"W3011696022","doi":"10.1109/tnsre.2020.2979412","title":"Transparent Electrophysiological Muscle Classification From EMG Signals Using Fuzzy-Based Multiple Instance Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Electrophysiology; Classifier (UML); Electromyography; Muscle fibre; Motor unit; Machine learning; Speech recognition; Neuroscience; Anatomy; Biology; Skeletal muscle","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":[0.001542721,0.0007257122,0.0009662519,0.000998645,0.0003471179,0.001329285,0.001389505,0.001055832,0.0009773327],"category_scores_gemma":[0.003792419,0.0002728725,0.001013569,0.0005894093,0.0004156019,0.001506106,0.001000788,0.001148399,0.0003781601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005144491,"about_ca_system_score_gemma":0.0005155612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001831414,"about_ca_topic_score_gemma":0.00190745,"domain_scores_codex":[0.9990811,0.0001860449,0.0001040401,0.0002662374,0.0002823545,0.0000801369],"domain_scores_gemma":[0.9986143,0.0005053135,0.0002635297,0.0002208561,0.0003311139,0.00006492655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005586713,0.0004329042,0.005329896,0.0001886148,0.0002460421,0.0002535912,0.0002102353,0.2115438,0.04562449,0.004667704,0.002115285,0.7288287],"study_design_scores_gemma":[0.000005556654,0.00005223417,0.0008130099,0.0000101033,0.0000181611,0.00004748963,0.00001719967,0.9909233,0.006113786,0.001707047,0.0002811663,0.00001090983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04012254,0.0001471015,0.9576764,0.0001039066,0.00003098516,0.00009004663,0.00008313074,0.000892423,0.0008535707],"genre_scores_gemma":[0.5926044,0.0001486021,0.405273,0.0001320297,0.00005430626,0.0001340229,0.000369501,0.00006405258,0.001220074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001831414,"threshold_uncertainty_score":0.008158743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03075929342901824,"score_gpt":0.22351101079376,"score_spread":0.1927517173647418,"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."}}