{"id":"W2105189995","doi":"10.1109/iembs.1990.692253","title":"Classification Performance Of Different Motor Unit Action Potential Feature Space Representations: A Simulation Study","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Action (physics); Space (punctuation); Feature (linguistics); Unit (ring theory); Artificial intelligence; Motor unit; Feature vector; Pattern recognition (psychology); Mathematics; Psychology; Physics; Neuroscience","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":[0.00007111227,0.00008938052,0.00009070042,0.0000763995,0.0001388385,0.00006233464,0.0002739155,0.0000383461,0.00001674653],"category_scores_gemma":[0.000006632758,0.00007315946,0.00003806383,0.0003404031,0.00001583903,0.0005407945,0.00006380794,0.00009513035,0.00001483338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002579616,"about_ca_system_score_gemma":0.00001211693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001007539,"about_ca_topic_score_gemma":0.00001854948,"domain_scores_codex":[0.9991668,0.00004708441,0.0001881933,0.0002587409,0.0002303124,0.0001088031],"domain_scores_gemma":[0.9991972,0.00005073178,0.0001374373,0.0004582714,0.0001152518,0.00004103832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001231532,0.003352605,0.08566647,0.00004404741,0.00008362067,0.000001081771,0.001259088,0.5576848,0.08515266,0.03516601,0.001833694,0.2296328],"study_design_scores_gemma":[0.0001743325,0.00008151505,0.3165937,0.000003194359,0.000007234207,8.236281e-7,0.00007243471,0.6814514,0.001281113,0.00005698642,0.0002216633,0.00005559418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.797116,0.0000078163,0.2003276,0.001710402,0.00006917162,0.0004459543,8.082105e-7,0.00008907733,0.0002331194],"genre_scores_gemma":[0.9933976,0.00001580192,0.004845882,0.00003610233,0.0001259257,0.00005235981,0.00000718756,0.000004968268,0.001514164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2309272,"threshold_uncertainty_score":0.2983356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0466846732869447,"score_gpt":0.3240385977579554,"score_spread":0.2773539244710107,"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."}}