{"id":"W2124670487","doi":"10.1016/j.medengphy.2007.06.005","title":"Motor unit potential characterization using “pattern discovery”","year":2007,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; University of Waterloo","funders":"","keywords":"Linear discriminant analysis; Naive Bayes classifier; Pattern recognition (psychology); Artificial intelligence; Computer science; Classifier (UML); Decision tree; Motor unit; Physical medicine and rehabilitation; Medicine; Psychology; Neuroscience; Support vector machine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001252795,0.0001705133,0.0001621024,0.00008329572,0.00005062344,0.00003455986,0.0001153871,0.00008427868,0.00002824751],"category_scores_gemma":[0.00002924535,0.000177278,0.00007204186,0.0003198123,0.00002211654,0.0002463857,0.00002687349,0.0002374958,0.000003067482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004332247,"about_ca_system_score_gemma":0.00001227815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008979566,"about_ca_topic_score_gemma":0.000001132734,"domain_scores_codex":[0.9989431,0.000004846201,0.0002159194,0.0001282637,0.000363151,0.0003447522],"domain_scores_gemma":[0.9996641,0.00003073553,0.00002337481,0.000126507,0.00002760232,0.0001276918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000130238,0.0001090559,0.001620824,0.0002840322,0.0002928458,0.00004772699,0.0003055367,0.03729054,0.6724705,0.000848009,0.00007989987,0.286638],"study_design_scores_gemma":[0.0004832841,0.0000357784,0.08501245,0.0001218989,0.00003553485,0.00001163373,0.00001923245,0.8889265,0.02169101,0.00003040357,0.003136546,0.0004957357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4010855,0.00002099205,0.5981135,0.00002224921,0.0004068923,0.00004867684,0.000004281838,0.0002445009,0.00005342237],"genre_scores_gemma":[0.9986683,0.00003304899,0.0003046327,0.00007300386,0.0008064356,0.000004626296,0.00004232205,0.00004590945,0.00002175553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8516359,"threshold_uncertainty_score":0.7229188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008339959376218354,"score_gpt":0.2101136660749951,"score_spread":0.2017737066987767,"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."}}