{"id":"W1975765066","doi":"10.1007/s11517-014-1170-x","title":"Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study","year":2014,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Science Council","keywords":"Amplitude; Electromyography; Cluster analysis; Root mean square; Probability density function; Pattern recognition (psychology); Computer science; Mathematics; Artificial intelligence; Engineering; Physics; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002484789,0.0001334932,0.0003281809,0.00013401,0.00008260246,0.000007320348,0.0001492656,0.0001100297,0.00004538299],"category_scores_gemma":[0.00155071,0.00009501381,0.0001565724,0.001241376,0.0000553596,0.00003714546,0.00006257491,0.0001823144,1.879308e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008176399,"about_ca_system_score_gemma":0.00001759034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001384159,"about_ca_topic_score_gemma":0.000006125958,"domain_scores_codex":[0.9982247,0.0002288514,0.0004748486,0.0002121388,0.0006910983,0.0001683006],"domain_scores_gemma":[0.998889,0.0004229033,0.0001189134,0.0002331144,0.0002864132,0.00004971563],"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":[0.00000359623,0.00007848795,0.05858319,0.00002435563,0.0002943882,2.245518e-8,0.0001187714,0.8923637,0.006012124,0.00006637986,8.078039e-7,0.04245413],"study_design_scores_gemma":[0.0001154072,0.00003137428,0.4822272,0.00001351332,0.0002302339,8.29375e-8,0.00002707369,0.517206,0.00006680089,0.00002990877,0.000004395335,0.00004799536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7970383,0.00003496438,0.2023992,0.00001149912,0.0001264558,0.0002546541,6.331833e-7,0.0000846764,0.00004960057],"genre_scores_gemma":[0.9995936,0.000001266594,0.0003201897,0.00001144804,0.00005494868,0.000006637832,0.0000046185,0.000007029688,2.225611e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.423644,"threshold_uncertainty_score":0.3874551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06623152418105646,"score_gpt":0.2871703592489436,"score_spread":0.2209388350678871,"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."}}