{"id":"W2912518300","doi":"10.3389/fnins.2019.00076","title":"MEG/EEG Group Analysis With Brainstorm","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":238,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Foundation for the National Institutes of Health","keywords":"Computer science; Executable; Scripting language; Brainstorming; Human–computer interaction; Interface (matter); Neuroinformatics; MATLAB; Artificial intelligence; Data science; Programming language; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003350459,0.0002214131,0.0003620572,0.0007098259,0.0002083867,0.00009193574,0.0006457979,0.0000455951,0.00002113285],"category_scores_gemma":[0.001864459,0.0001867977,0.0001003646,0.004709741,0.0004715576,0.0006548024,0.0001737872,0.0002625531,0.00003149989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001265567,"about_ca_system_score_gemma":0.00005355986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002651468,"about_ca_topic_score_gemma":0.00006386241,"domain_scores_codex":[0.99712,0.0001446837,0.0002101169,0.001260543,0.0007416582,0.0005229797],"domain_scores_gemma":[0.9984957,0.0007162896,0.0001100331,0.000554372,0.00002744661,0.00009615281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001456046,0.0001863675,0.8591619,0.00001812153,0.00001638035,0.00008589616,0.0003118745,0.005010462,0.125753,0.00285709,0.005414254,0.001039067],"study_design_scores_gemma":[0.001330268,0.0008139012,0.8867161,0.00002876761,0.00008360614,0.000039735,0.0003416029,0.04099334,0.01027713,0.0007591805,0.05775448,0.0008619432],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685075,0.00005397849,0.01602378,0.002804257,0.003319801,0.0005048571,0.0000152686,0.0001627295,0.008607819],"genre_scores_gemma":[0.9905195,0.00003069064,0.001115291,0.006945487,0.00002157798,0.00002924585,6.441407e-7,0.00001763467,0.001319908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1154759,"threshold_uncertainty_score":0.7617389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468665184746176,"score_gpt":0.225205469423096,"score_spread":0.2105188175756342,"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."}}