{"id":"W1896390572","doi":"10.1002/mus.23977","title":"Feature selection for motor unit potential train characterization","year":2013,"lang":"en","type":"article","venue":"Muscle & Nerve","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Motor unit; Pattern recognition (psychology); Feature (linguistics); Feature selection; Artificial intelligence; Stability (learning theory); Computer science; Selection (genetic algorithm); Neuroscience; Machine learning; Psychology","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":[0.001138488,0.001018423,0.0009904904,0.001944597,0.0004342429,0.0006558016,0.0007311044,0.0005115187,0.003752928],"category_scores_gemma":[0.004186621,0.0001823089,0.0008069467,0.001571935,0.0002713892,0.0004798374,0.000458924,0.0004813353,0.001076636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002845394,"about_ca_system_score_gemma":0.0008189821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002430114,"about_ca_topic_score_gemma":0.00220943,"domain_scores_codex":[0.9994239,0.0001610571,0.00006716586,0.0001225608,0.0001590055,0.00006628164],"domain_scores_gemma":[0.9982091,0.0009836019,0.0001266798,0.00009463704,0.0005186021,0.00006735732],"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":[0.001159982,0.0003288311,0.02514661,0.000362555,0.0002044024,0.0003142723,0.0001647897,0.01845573,0.08004709,0.0005975318,0.00469315,0.8685249],"study_design_scores_gemma":[0.0002559829,0.001189848,0.09489081,0.0001123323,0.0005266239,0.001139125,0.0003344316,0.80115,0.086223,0.002984602,0.01107457,0.000118623],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3339567,0.0007264171,0.6579337,0.0001997369,0.00006727463,0.0005407156,0.002208019,0.002367849,0.00199962],"genre_scores_gemma":[0.6942691,0.0001627997,0.2981325,0.00007186311,0.00005402266,0.0008550786,0.004729043,0.000178471,0.001547077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003752928,"threshold_uncertainty_score":0.01255476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008965161783753695,"score_gpt":0.1973686101045059,"score_spread":0.1884034483207522,"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."}}