{"id":"W3048339216","doi":"10.3389/fninf.2020.00033","title":"A Quantitative EEG Toolbox for the MNI Neuroinformatics Ecosystem: Normative SPM of EEG Source Spectra","year":2020,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Neuroinformatics; Toolbox; Computer science; Electroencephalography; Statistical parametric mapping; Normative; Parametric statistics; Artificial intelligence; Data science; Statistics; Psychology; Neuroscience; Medicine; Magnetic resonance imaging","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004127391,0.0004118289,0.0006811271,0.00023356,0.0002375311,0.0002063853,0.001347108,0.00009832622,0.000009411686],"category_scores_gemma":[0.001868206,0.0003098797,0.0002521369,0.0008002025,0.0002524707,0.001191302,0.0002259934,0.0005555634,0.00004000952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005390137,"about_ca_system_score_gemma":0.0001103066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004096563,"about_ca_topic_score_gemma":0.000005354346,"domain_scores_codex":[0.9967899,0.0001650762,0.0015402,0.000296545,0.0005922724,0.0006159595],"domain_scores_gemma":[0.9965925,0.001715791,0.0008889452,0.0005274958,0.0001081795,0.0001670682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00285755,0.0007016846,0.00260385,0.009035158,0.0003546176,0.0000827538,0.3669857,0.2200458,0.01832638,0.02833657,0.3214871,0.02918275],"study_design_scores_gemma":[0.001113802,0.0009246314,0.0002018785,0.0001091287,0.00004190291,0.00002933424,0.009628085,0.9420121,0.01899885,0.0003208929,0.02626539,0.0003540484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2577634,0.0001676007,0.7255328,0.003429932,0.002713937,0.003236684,0.0004980012,0.0002527866,0.006404949],"genre_scores_gemma":[0.9373029,0.0002000094,0.05484951,0.007269774,0.00009626071,0.00006740468,0.00001311203,0.00007536133,0.0001256794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7219662,"threshold_uncertainty_score":0.9999353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03528267468865764,"score_gpt":0.2606497419070785,"score_spread":0.2253670672184209,"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."}}