{"id":"W2153260976","doi":"10.1109/iembs.2009.5332849","title":"Validation of motor unit potential trains using motor unit firing pattern information","year":2009,"lang":"en","type":"article","venue":"","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Motor unit; Train; Computer science; Classifier (UML); Pattern recognition (psychology); Artificial intelligence; Word error rate; Speech recognition; Machine learning","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":[0.006628011,0.0007356079,0.0007704433,0.001532362,0.0002648379,0.0007897892,0.0008912188,0.001134133,0.0007196674],"category_scores_gemma":[0.03828491,0.0002406807,0.0004320345,0.0006334236,0.000561657,0.001229377,0.0006506013,0.000683392,0.0006770774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003455319,"about_ca_system_score_gemma":0.0005354268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001160598,"about_ca_topic_score_gemma":0.001221155,"domain_scores_codex":[0.996445,0.001084272,0.0003128399,0.0005581019,0.001447239,0.0001524816],"domain_scores_gemma":[0.9752387,0.01410978,0.002720343,0.001975757,0.005557592,0.0003979195],"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":[0.002393324,0.0003753772,0.099587,0.0004430886,0.000423392,0.0003896082,0.0004063577,0.07471059,0.1994831,0.001201743,0.000682921,0.6199036],"study_design_scores_gemma":[0.00007006613,0.001431323,0.09952076,0.00006834664,0.00008285093,0.0008746034,0.000106809,0.7881362,0.1074133,0.0009752097,0.001238013,0.00008246907],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3994646,0.0003417926,0.5978217,0.00005745455,0.00005547156,0.0001893101,0.0003346642,0.000888724,0.0008463329],"genre_scores_gemma":[0.8572507,0.0001164492,0.1410232,0.00004251743,0.00002366284,0.000169044,0.0006562248,0.00008278227,0.000635299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006628011,"threshold_uncertainty_score":0.03505266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789568978874677,"score_gpt":0.2252165665064073,"score_spread":0.2073208767176605,"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."}}