{"id":"W4385295263","doi":"10.1002/hbm.26431","title":"Modeling venous bias in resting state functional <scp>MRI</scp> metrics","year":2023,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Heart Institute; Montreal Neurological Institute and Hospital; McGill University Health Centre; Concordia University","funders":"National Institute of Neurological Disorders and Stroke; Max-Planck-Gesellschaft; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Réseau en Bio-Imagerie du Quebec; Heart and Stroke Foundation of Canada","keywords":"Amplitude; Voxel; Homogeneity (statistics); Nuclear magnetic resonance; Functional magnetic resonance imaging; Blood-oxygen-level dependent; Physics; Resting state fMRI; Mathematics; Statistics; Computer science; Neuroscience; Artificial intelligence; Psychology; Optics","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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001935657,0.000264922,0.0003150109,0.001425334,0.001054854,0.0001463024,0.0002743029,0.00007602899,0.00001237256],"category_scores_gemma":[0.05395641,0.0003016938,0.0001037225,0.003388713,0.0001042925,0.0003334685,0.0003742939,0.0005003629,0.000276836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002327074,"about_ca_system_score_gemma":0.00007859393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001006897,"about_ca_topic_score_gemma":0.0001222293,"domain_scores_codex":[0.9968318,0.0003080005,0.000485856,0.000892208,0.0007290354,0.0007531047],"domain_scores_gemma":[0.9835502,0.0158212,0.0001309865,0.0003161281,0.0000907518,0.00009072172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001157929,0.0001742502,0.008918754,0.0001592362,0.00003521537,0.0003412525,0.00554198,0.6545026,0.2988162,0.006278899,0.02405503,0.001164949],"study_design_scores_gemma":[0.001684506,0.0002224121,0.1071154,0.0003581508,0.00001318101,0.00006137546,0.003739833,0.8162588,0.001953632,0.05040648,0.0176585,0.000527636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849393,0.0000665765,0.007527958,0.002081655,0.0006069375,0.0003152126,0.00001380418,0.0006659119,0.003782582],"genre_scores_gemma":[0.9938479,0.00001918546,0.000202265,0.002616204,0.0002793491,0.00006973446,0.00001222827,0.00005776071,0.002895386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2968626,"threshold_uncertainty_score":0.9999435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2283133975818356,"score_gpt":0.3084260697732895,"score_spread":0.08011267219145393,"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."}}