{"id":"W2171871795","doi":"10.1016/j.mri.2009.05.034","title":"Bootstrap generation and evaluation of an fMRI simulation database","year":2009,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Autocorrelation; Computer science; Functional magnetic resonance imaging; Parametric statistics; Noise (video); Variance (accounting); Statistical model; Artificial intelligence; Process (computing); Spatial analysis; Database; Data mining; Statistics; Mathematics","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":[0.0005917575,0.00009498581,0.00009608604,0.00008077171,0.0001388841,0.00004364178,0.00006690979,0.00001478866,0.00003519292],"category_scores_gemma":[0.002710606,0.00009836906,0.00001557135,0.0001688633,0.00008441033,0.0005378599,0.00002297959,0.00005877172,0.000002616497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003092906,"about_ca_system_score_gemma":0.0000306311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001517285,"about_ca_topic_score_gemma":0.000009178925,"domain_scores_codex":[0.9986262,0.0001908643,0.0001747314,0.0003831183,0.0004933078,0.0001317531],"domain_scores_gemma":[0.9990907,0.0004556048,0.00007066471,0.0002213494,0.0001300969,0.00003160011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001912784,0.00004608239,0.000886008,0.000004194689,2.571181e-7,0.000001981907,0.0001335841,0.006646712,0.3734056,0.0004759102,0.0001437603,0.6182368],"study_design_scores_gemma":[0.000486279,0.0001110633,0.1077107,0.00001517677,0.00001531912,0.000007661585,0.00002661503,0.849947,0.0393393,0.001232833,0.00100954,0.00009850423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932219,0.002642484,0.0009602291,0.001835873,0.0001111521,0.0002955053,0.00001093022,0.00003749887,0.0008843801],"genre_scores_gemma":[0.9981545,0.00003219164,0.0007403239,0.0009090035,0.00009914675,0.00001059331,0.000006185966,0.000006373906,0.00004166742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8433003,"threshold_uncertainty_score":0.4011374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09317761207951746,"score_gpt":0.3464850799257326,"score_spread":0.2533074678462151,"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."}}