{"id":"W2152767489","doi":"10.1109/tbme.2008.918439","title":"Investigation of Short-Term Changes in Visual Evoked Potentials With Windowed Adaptive Chirplet Transform","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"SIGNAL (programming language); Computer science; Pattern recognition (psychology); Chirp; Artificial intelligence; Transient (computer programming); Speech recognition; Time–frequency analysis; Feature extraction; Segmentation; Signal processing; Term (time); Feature (linguistics); Instantaneous phase; Computer vision; Optics; Physics","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":[0.0001652962,0.0001756692,0.000141456,0.0003435348,0.00007679504,0.0002040414,0.000182242,0.0002371258,0.0004898874],"category_scores_gemma":[0.0005838617,0.00009247174,0.0001178622,0.0004628581,0.0002011002,0.0004724035,0.0001397574,0.0002219133,0.00007061852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005996204,"about_ca_system_score_gemma":0.00007212712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002774209,"about_ca_topic_score_gemma":0.0002647452,"domain_scores_codex":[0.9999397,0.000009922994,0.000002709574,0.00001092921,0.00003078205,0.000005948997],"domain_scores_gemma":[0.9998268,0.00009192686,0.00002559013,0.00001365821,0.00003192872,0.000009984884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002864994,0.00004063876,0.002510824,0.0001118267,0.00002880165,0.0007304419,0.000119673,0.007813636,0.9096981,0.001550103,0.0001897163,0.07691973],"study_design_scores_gemma":[0.00005820861,0.0007547024,0.05348876,0.00003608982,0.0000764283,0.002195818,0.0001756274,0.5201655,0.4162292,0.002863209,0.003903321,0.00005316037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7704822,0.001320909,0.2256713,0.0001289155,0.00003889357,0.00004885764,0.00008864077,0.0001764004,0.002043946],"genre_scores_gemma":[0.9379002,0.0007759568,0.06047469,0.00002526727,0.00004225729,0.00002794196,0.00007038179,0.00002483854,0.0006584571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004898874,"threshold_uncertainty_score":0.00163883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056197527131659,"score_gpt":0.237044817963383,"score_spread":0.2164828426920664,"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."}}