{"id":"W2535624273","doi":"10.1109/embc.2016.7592195","title":"Surrogate analysis of fractal dimensions from SEMG sensor array as a predictor of chronic low back pain","year":2016,"lang":"en","type":"article","venue":"","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Fractal; Fractal dimension; Nonlinear system; Fast Fourier transform; Surrogate data; Fractal analysis; Artificial neural network; Pattern recognition (psychology); SIGNAL (programming language); Computer science; Dimension (graph theory); Artificial intelligence; Mathematics; Algorithm; Physics; Mathematical analysis","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":[0.000653278,0.0003134317,0.0004077565,0.0006385883,0.0000904781,0.0003117242,0.0001493932,0.0003795868,0.0005522225],"category_scores_gemma":[0.003346474,0.00009224856,0.0002765526,0.0003132907,0.0001639181,0.0003009232,0.0001902504,0.0002980818,0.0001547708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001069779,"about_ca_system_score_gemma":0.0001400004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001726313,"about_ca_topic_score_gemma":0.0002651587,"domain_scores_codex":[0.9997219,0.0001128526,0.00001747445,0.00004360943,0.00008699328,0.0000171006],"domain_scores_gemma":[0.9989108,0.0006776868,0.0001255305,0.00008186465,0.000160032,0.00004416899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001762194,0.0004225618,0.0669161,0.0004814614,0.0004073197,0.0007132234,0.0002554668,0.1625718,0.2986994,0.002826521,0.001838916,0.4631051],"study_design_scores_gemma":[0.00002209814,0.0005281794,0.07255389,0.00002767238,0.00004862265,0.0009199663,0.00005125809,0.8989971,0.02357879,0.002440113,0.0007810479,0.00005127491],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5015956,0.0009138453,0.4954138,0.0002155477,0.0001164362,0.00005065391,0.000380852,0.0003217562,0.0009915234],"genre_scores_gemma":[0.9451658,0.000272475,0.05366836,0.000035783,0.00004496836,0.00004381032,0.0003364568,0.00001822338,0.0004142037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000653278,"threshold_uncertainty_score":0.003454924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009297940063975092,"score_gpt":0.2397603882062632,"score_spread":0.2304624481422881,"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."}}