{"id":"W2147716763","doi":"10.1109/tbme.2012.2190601","title":"Morphology-Based Automatic Seizure Detector for Intracerebral EEG Recordings","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Electroencephalography; Waveform; Sensitivity (control systems); Channel (broadcasting); Pattern recognition (psychology); Computer science; Artificial intelligence; Epilepsy; Audiology; Speech recognition; Neuroscience; Psychology; Medicine; Electronic engineering; Engineering; Telecommunications","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":[0.0001782151,0.0002055318,0.0002037017,0.0002531009,0.0001192718,0.00003384548,0.0002258724,0.0001622585,0.0001820351],"category_scores_gemma":[0.00007286631,0.0001827443,0.0001383928,0.0003130376,0.00008405049,0.0001708587,0.000001647979,0.0003252311,0.00006273948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007146835,"about_ca_system_score_gemma":0.00002614769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003547083,"about_ca_topic_score_gemma":4.358515e-7,"domain_scores_codex":[0.9986782,0.00002727404,0.0002584713,0.0002839843,0.0002175091,0.0005345718],"domain_scores_gemma":[0.9988986,0.0005879287,0.00004140165,0.0001845343,0.00001436556,0.0002731334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003313168,0.0003343323,0.000003267508,0.0001485353,0.00002311202,0.000006959122,0.0002113742,0.009350816,0.9365365,0.00004107429,0.0007464058,0.05256452],"study_design_scores_gemma":[0.000548177,0.0002137748,0.00003412377,0.00006571713,0.00001919261,0.00004876312,0.000008896122,0.406672,0.5869678,0.000007503056,0.005206089,0.0002079734],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3212239,0.00001001924,0.6755699,0.0004506785,0.002167648,0.0001909423,0.00003794964,0.0003387352,0.00001028791],"genre_scores_gemma":[0.9846486,0.000002357041,0.01440846,0.0005380145,0.0001760155,0.0001121577,0.00000168007,0.00003664724,0.00007600919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6634248,"threshold_uncertainty_score":0.7452098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01858078217184271,"score_gpt":0.2501723989611878,"score_spread":0.231591616789345,"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."}}