{"id":"W1975457508","doi":"10.1007/s00421-002-0685-2","title":"Recovery of electromyograph median frequency after lumbar muscle fatigue analysed using an exponential time dependence model","year":2002,"lang":"en","type":"article","venue":"European Journal of Applied Physiology","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Karolinska Institutet; McGill University","keywords":"Isometric exercise; Mathematics; Electromyography; Lumbar; Exponential function; Linear regression; Muscle contraction; Muscle fatigue; Contraction (grammar); Statistics; Medicine; Anatomy; Physical medicine and rehabilitation; Physical therapy; Mathematical analysis; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004092917,0.0003224205,0.0003394498,0.0002718395,0.000125603,0.0002471438,0.0003446721,0.000515904,0.001563108],"category_scores_gemma":[0.001784628,0.0001762635,0.0005451132,0.0002233648,0.0001519285,0.0003303901,0.0002000575,0.0003882457,0.0004438465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002502051,"about_ca_system_score_gemma":0.000255518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004289206,"about_ca_topic_score_gemma":0.00262336,"domain_scores_codex":[0.9998987,0.00001858116,0.000007189451,0.00002693098,0.00002468395,0.00002395924],"domain_scores_gemma":[0.9995419,0.0003223918,0.00003932786,0.00004100632,0.00004469856,0.00001070954],"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":[0.003106393,0.000392303,0.02037124,0.000592652,0.0003438353,0.000746719,0.0005631762,0.6643497,0.2005744,0.002944268,0.0007637655,0.1052516],"study_design_scores_gemma":[0.00001820143,0.0002462523,0.02002764,0.00002095176,0.00004725975,0.0001869124,0.00003596224,0.9692137,0.009225696,0.0004549208,0.0005004497,0.00002215109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7480341,0.0004495417,0.2488865,0.00008795788,0.00002126369,0.00004904364,0.0002710397,0.0005621678,0.001638475],"genre_scores_gemma":[0.9921537,0.0001170919,0.005976542,0.000008796403,0.000003200644,0.00002828206,0.0001511756,0.00004075002,0.001520396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004289206,"threshold_uncertainty_score":0.008528471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222691770122376,"score_gpt":0.2092442793599212,"score_spread":0.1870173616586975,"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."}}