{"id":"W2084511243","doi":"10.1016/j.jelekin.2010.07.010","title":"Multiplicative multi-fractal modeling of electromyography signals for discerning neuropathic conditions","year":2010,"lang":"en","type":"article","venue":"Journal of Electromyography and Kinesiology","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Toronto Metropolitan University; McGill University","funders":"","keywords":"Fractal; Electromyography; Cascade; Multiplicative function; Noise (video); Computer science; Pattern recognition (psychology); SIGNAL (programming language); Artificial intelligence; Mathematics; Medicine; Mathematical analysis; Engineering; Physical medicine and rehabilitation","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":[0.0003243551,0.0003507831,0.0002665582,0.0007633978,0.0001454918,0.0003692032,0.0002421308,0.0004579808,0.0004282638],"category_scores_gemma":[0.001375987,0.000126013,0.0004409018,0.0003573064,0.000204262,0.0004599712,0.0002161175,0.0002932517,0.0001112535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001786543,"about_ca_system_score_gemma":0.0001218301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00065662,"about_ca_topic_score_gemma":0.0008749882,"domain_scores_codex":[0.9999211,0.00002808679,0.000005832106,0.00001447016,0.00002165178,0.000008785983],"domain_scores_gemma":[0.9995843,0.0002558603,0.00005077476,0.00003350842,0.00005714997,0.00001839919],"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":[0.0003371983,0.0001367702,0.00871177,0.0002071121,0.0001183056,0.000657393,0.0002480943,0.7016641,0.09067573,0.01585376,0.0008721275,0.1805177],"study_design_scores_gemma":[0.000002184719,0.00003665769,0.002578108,0.000005062976,0.00001301629,0.000144423,0.00001143495,0.9945464,0.001003,0.001487179,0.0001643684,0.000008231882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3254427,0.0009038372,0.6713237,0.0002012309,0.00007186017,0.00003669978,0.00009747748,0.0001633443,0.001759178],"genre_scores_gemma":[0.9591458,0.0003374223,0.03978792,0.00001850985,0.00003655501,0.00001835397,0.00005631551,0.00001908596,0.0005800671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007633978,"threshold_uncertainty_score":0.001715362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376744850412162,"score_gpt":0.2524573907951559,"score_spread":0.2386899422910343,"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."}}