{"id":"W4389474469","doi":"10.1109/csci43386.2017.10350017","title":"Retraction Notice: 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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sheridan College","funders":"","keywords":"Support vector machine; Electroencephalography; Ictal; Computer science; Pattern recognition (psychology); Migraine; Migraine with aura; Artificial intelligence; Aura; Feature selection; 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":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002740101,0.0002160837,0.0002205731,0.0001485421,0.0007068308,0.0002956031,0.0005085174,0.00007478705,0.000008988342],"category_scores_gemma":[0.0005806643,0.0001590005,0.00007378742,0.0002664328,0.00009997572,0.0004376917,0.00007837363,0.0002410029,0.000006390934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002860255,"about_ca_system_score_gemma":0.00005110668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001986218,"about_ca_topic_score_gemma":0.00003543056,"domain_scores_codex":[0.9984029,0.00002624888,0.0002199184,0.0006209714,0.0002547895,0.0004751688],"domain_scores_gemma":[0.9988611,0.0002200425,0.0002018327,0.0004700069,0.0001136738,0.0001333223],"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":[0.0002541484,0.0002723216,0.007481608,0.00008423324,0.00001947639,0.0000148912,0.0003346072,0.00105764,0.9870662,0.0002843656,0.0008745987,0.002255903],"study_design_scores_gemma":[0.001123544,0.002335834,0.009671166,0.00007761592,0.00005200082,0.0003106489,0.00005771017,0.538722,0.4456221,0.0000756853,0.001401593,0.0005500273],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7846821,0.000002738811,0.2060248,0.0005427335,0.000330631,0.0007939469,0.00001922814,0.0001522487,0.007451524],"genre_scores_gemma":[0.8665838,3.454687e-7,0.1320411,0.0006191923,0.0002484984,0.00002785995,0.000003692467,0.00002748411,0.0004480185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5414441,"threshold_uncertainty_score":0.6483853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0682940617041142,"score_gpt":0.3198392283647865,"score_spread":0.2515451666606723,"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."}}