{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000432966,0.0001293498,0.000113974,0.0001109139,0.0001222373,0.00006239864,0.00006232558,0.00008394945,0.0001039186],"category_scores_gemma":[0.00001121514,0.0001302933,0.00008523525,0.0002488409,0.00000963491,0.000263738,0.000006574768,0.00009945215,0.000007625729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002708647,"about_ca_system_score_gemma":0.000005631779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001942359,"about_ca_topic_score_gemma":0.0000105253,"domain_scores_codex":[0.9994248,0.00001409624,0.0001029991,0.0001347985,0.00008530072,0.0002380446],"domain_scores_gemma":[0.9997371,0.00001542266,0.00002814074,0.00008189015,0.00008999094,0.00004740214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000007317113,0.00002000879,0.0002398967,0.00005042352,0.0000591524,1.622906e-7,0.0001465493,0.0001588099,0.8681143,0.0001506447,0.006871511,0.1241813],"study_design_scores_gemma":[0.0007685747,0.000144704,0.8542063,0.00001840714,0.00003258016,0.000003346076,0.00009267127,0.06347343,0.01722778,0.0002039386,0.06344999,0.0003783464],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9599203,0.00004767999,0.03703158,0.001004081,0.0003757749,0.0006357139,0.00002736308,0.0004937226,0.0004637246],"genre_scores_gemma":[0.9977427,0.00002047601,0.0005910454,0.0001005126,0.0003057078,0.0002888133,0.0001440269,0.0000340726,0.000772717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8539664,"threshold_uncertainty_score":0.5313208,"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."}}