{"id":"W1968240874","doi":"10.2316/p.2013.791-100","title":"A Time-Series Pre-Processing Methodology for Biosignal Classification using Statistical Feature Extraction","year":2013,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Biosignal; Computer science; Feature extraction; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Data mining; Computer vision","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":[0.0003604239,0.0001159453,0.0001823084,0.00008383125,0.0002622487,0.0003257063,0.000224699,0.00009497017,0.0002091749],"category_scores_gemma":[0.0001276643,0.00009316171,0.00005786605,0.0002453616,0.0000684064,0.001247969,0.00005893262,0.00009054992,0.00002675099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003604821,"about_ca_system_score_gemma":0.00005014845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005404308,"about_ca_topic_score_gemma":0.000006014487,"domain_scores_codex":[0.9989755,0.00009260255,0.0002185794,0.0003416114,0.0001262626,0.0002454865],"domain_scores_gemma":[0.999157,0.0002289221,0.0001520854,0.0001942201,0.0002022485,0.00006555911],"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":[0.00004020437,0.00005943047,0.0001214593,0.00006027344,0.00005145933,0.000001292639,0.0005639147,0.0005745257,0.3053839,0.06794777,0.003553655,0.6216422],"study_design_scores_gemma":[0.00008911044,0.00007389188,0.00170248,0.000008965456,0.00002345935,0.00003192619,0.0001519096,0.988829,0.002905337,0.00478826,0.001259899,0.000135746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003736584,0.00004889072,0.9944144,0.0009764243,0.0000785529,0.0001898398,0.000002814663,0.0001067778,0.0004456872],"genre_scores_gemma":[0.07577865,0.000001518907,0.922611,0.00008498305,0.00009382627,0.00002802148,0.00001223771,0.000008939208,0.001380843],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9882545,"threshold_uncertainty_score":0.3799025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09139368478324184,"score_gpt":0.3399540269801976,"score_spread":0.2485603421969557,"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."}}