{"id":"W2950568021","doi":"10.1016/j.neuroimage.2016.06.034","title":"The Neuro Bureau ADHD-200 Preprocessed repository","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":355,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Resting state fMRI; Neuroimaging; Computer science; Functional magnetic resonance imaging; Preprocessor; Artificial intelligence; Machine learning; Psychology; Neuroscience","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0001870845,0.0001759562,0.0001244647,0.00003976458,0.000792443,0.0001153357,0.000459585,0.00003433284,0.00001655814],"category_scores_gemma":[0.02465646,0.00009578411,0.00008302627,0.0002320921,0.0003851887,0.0002976648,0.0002145863,0.0001569372,0.0002648448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003212433,"about_ca_system_score_gemma":0.00004509991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009544664,"about_ca_topic_score_gemma":0.000004442861,"domain_scores_codex":[0.9980118,0.0002434264,0.0002022765,0.0007731059,0.0004100741,0.0003593364],"domain_scores_gemma":[0.9851973,0.01385242,0.0001024934,0.0007086061,0.00006911039,0.00007007161],"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":[0.00007670773,0.00003958196,0.0004419296,0.000006710677,0.000003203575,0.00009866673,0.00005767514,0.00000227429,0.9462692,0.001362891,0.04896545,0.00267568],"study_design_scores_gemma":[0.0005649093,0.000202643,0.01062804,0.00001876517,0.00001140052,0.0002016622,0.0000232308,0.00006268357,0.6114377,0.003475045,0.3731242,0.0002496971],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.68045,0.0002653527,0.0006314701,0.1584831,0.006983205,0.001064783,0.00003546713,0.001415797,0.1506708],"genre_scores_gemma":[0.9776999,0.00005504557,0.0000126479,0.005931612,0.0004059748,0.00005636971,1.103827e-7,0.0000342622,0.01580401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3348315,"threshold_uncertainty_score":0.9835593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04547474068108227,"score_gpt":0.2795789733233292,"score_spread":0.2341042326422469,"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."}}