{"id":"W2905365269","doi":"10.1109/csci.2017.298","title":"Retracted: A Non-Linear Support Vector Machine Approach to Testing for Migraine with Aura Using Electroencephalography","year":2017,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":true,"has_abstract":true,"ca_institutions":"Sheridan College","funders":"","keywords":"Support vector machine; Electroencephalography; Ictal; Pattern recognition (psychology); Computer science; Artificial intelligence; Migraine; Migraine with aura; Aura; Feature selection; Classifier (UML); Speech recognition; Psychology; Neuroscience","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001475505,0.0008220887,0.0007528383,0.001026131,0.0003381361,0.0007087995,0.001434853,0.000856202,0.002338699],"category_scores_gemma":[0.005824867,0.0002160361,0.0006479658,0.0004835694,0.0003152039,0.0007646417,0.000777885,0.00113987,0.0009438319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002708198,"about_ca_system_score_gemma":0.0005318517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002675617,"about_ca_topic_score_gemma":0.002145101,"domain_scores_codex":[0.9989877,0.0003177041,0.00009531678,0.0002320887,0.0002860285,0.00008112298],"domain_scores_gemma":[0.9984103,0.0006855592,0.0001138814,0.0001742609,0.000547432,0.00006844645],"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":[0.0003667409,0.0002242129,0.01179263,0.000126857,0.0001474696,0.0004432756,0.0001696068,0.0364067,0.01206501,0.001188791,0.002260439,0.9348082],"study_design_scores_gemma":[0.00003651559,0.0006171223,0.009943862,0.00003404829,0.00005075759,0.0003759054,0.0001269553,0.9759518,0.006989264,0.003024451,0.002818299,0.00003106066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09900651,0.000901836,0.8948717,0.0006100911,0.0002971137,0.0002317647,0.0002830929,0.001920387,0.001877455],"genre_scores_gemma":[0.7799018,0.0002753193,0.2137853,0.0002916322,0.000215659,0.0002271873,0.0007141416,0.00008691889,0.004502014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9991438,"threshold_uncertainty_score":0.007823765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06637986296740554,"score_gpt":0.3092050238346845,"score_spread":0.2428251608672789,"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."}}