{"id":"W2925215685","doi":"10.3389/fnins.2019.00284","title":"Brainstorm Pipeline Analysis of Resting-State Data From the Open MEG Archive","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; AXA Research Fund; Fondation Brain Canada","keywords":"Pipeline (software); Computer science; Brainstorming; Magnetoencephalography; Resting state fMRI; Generalizability theory; Workflow; Proof of concept; Scalability; Data mining; Artificial intelligence; Psychology; Neuroscience; Electroencephalography; Database","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001017128,0.0001765105,0.0004342132,0.000305172,0.0002174333,0.000132539,0.004805592,0.00002488656,0.00001850144],"category_scores_gemma":[0.02063029,0.0001332642,0.00006864915,0.003228481,0.0006784101,0.0007763305,0.003046162,0.0002775812,0.000009949885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004462381,"about_ca_system_score_gemma":0.0001230953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006432403,"about_ca_topic_score_gemma":0.0007306768,"domain_scores_codex":[0.996866,0.0003918267,0.0003757008,0.001378407,0.0006340119,0.0003540944],"domain_scores_gemma":[0.9925172,0.005305556,0.0002650863,0.001816471,0.0000363927,0.00005929343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002477994,0.0002112999,0.731348,0.00001023899,0.00003808058,0.000030156,0.001124716,0.01234055,0.1614455,0.0004762902,0.09023272,0.002494642],"study_design_scores_gemma":[0.0006149088,0.0001082934,0.5363764,0.00003576537,0.00009616368,0.000002510246,0.0002277102,0.4246007,0.005795345,0.002350148,0.02948928,0.0003028361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547492,0.0001409244,0.02273289,0.006811208,0.005195121,0.001265145,0.002136237,0.00006930713,0.006899975],"genre_scores_gemma":[0.9893772,0.000073072,0.00165052,0.007994525,0.00003145992,0.00001389825,0.00001943016,0.00001522863,0.0008246628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4122601,"threshold_uncertainty_score":0.9876193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07673805170789713,"score_gpt":0.3060668015973581,"score_spread":0.229328749889461,"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."}}