{"id":"W2594578343","doi":"10.6000/1929-6029.2017.06.01.2","title":"Model Based Sparse Feature Extraction for Biomedical Signal Classification","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Principal component analysis; Sparse approximation; Artificial intelligence; Computer science; SIGNAL (programming language); Feature extraction; Feature (linguistics); Signal processing; Signal reconstruction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007671739,0.0007660394,0.001078213,0.001015518,0.0002909653,0.00071239,0.0006513142,0.0008193596,0.001731916],"category_scores_gemma":[0.002913947,0.0003103198,0.001112834,0.001575828,0.0004276339,0.0009473713,0.0006809533,0.001261791,0.0009766974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003551881,"about_ca_system_score_gemma":0.0005651472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001466014,"about_ca_topic_score_gemma":0.001215072,"domain_scores_codex":[0.9995414,0.0001424016,0.00002876538,0.00008717613,0.0001663658,0.00003403483],"domain_scores_gemma":[0.9993338,0.0003401743,0.0000854746,0.00009061885,0.0001339844,0.00001591639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001401506,0.0001159617,0.0009453135,0.0003749512,0.0001690292,0.0001581719,0.0001070456,0.3056726,0.04007348,0.02626848,0.006809654,0.6191652],"study_design_scores_gemma":[0.000004797824,0.00003990497,0.0003291473,0.00001333553,0.0000157445,0.00006338496,0.000008530115,0.9879066,0.002646463,0.007042811,0.001917255,0.00001197827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00215018,0.0003011258,0.9968724,0.0001053423,0.00002202231,0.00001654803,0.00005956497,0.0002229654,0.0002499563],"genre_scores_gemma":[0.2704801,0.002728977,0.721814,0.0002243356,0.000305272,0.0003208635,0.001183018,0.0001547463,0.002788636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001731916,"threshold_uncertainty_score":0.00579381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1900171270802646,"score_gpt":0.5168197255182762,"score_spread":0.3268025984380116,"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."}}