{"id":"W2100435000","doi":"10.1016/j.yebeh.2009.11.017","title":"A common strategy and database to compare the performance of seizure prediction algorithms","year":2009,"lang":"en","type":"article","venue":"Epilepsy & Behavior","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Milestone; Epilepsy; Set (abstract data type); Computer science; Competition (biology); Epileptic seizure; Data set; Algorithm; Artificial intelligence; Machine learning; Data mining; Psychology; Psychiatry","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":[0.0001297873,0.0001323611,0.000163557,0.00005327276,0.0001557108,0.00005608586,0.0003003427,0.00002604113,0.00002662516],"category_scores_gemma":[0.00001429058,0.00009290902,0.00003066626,0.0001838718,0.00008897081,0.0002135385,0.00007325746,0.0001852389,0.00001121899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001392589,"about_ca_system_score_gemma":0.0000169474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000337808,"about_ca_topic_score_gemma":0.00001241351,"domain_scores_codex":[0.9989464,0.00007274656,0.0002349332,0.0002944373,0.0002245191,0.0002269745],"domain_scores_gemma":[0.9993997,0.00006636815,0.00007396537,0.0003347816,0.00002924648,0.00009599993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001331686,0.000718513,0.1298123,0.00003420103,0.000005145075,0.00004952566,0.002093273,0.0005437374,0.6664209,0.0003694265,0.005770317,0.1940494],"study_design_scores_gemma":[0.0004081556,0.001100939,0.6560999,0.0001042418,0.00003854582,0.0001172414,0.0001755714,0.008516263,0.3308769,0.000009240432,0.002365624,0.0001874879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984756,0.00004723436,0.00008275749,0.0003178654,0.0001861073,0.0003749269,0.0001884753,0.00005844804,0.000268549],"genre_scores_gemma":[0.9988852,0.00002390687,0.0002536188,0.0005372017,0.0000727074,0.00002151887,0.00001105152,0.000007438815,0.0001874222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5262875,"threshold_uncertainty_score":0.378872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04027278248288022,"score_gpt":0.3048510519164048,"score_spread":0.2645782694335246,"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."}}