{"id":"W2271529269","doi":"10.1177/0271678x16631755","title":"Perfusion information extracted from resting state functional magnetic resonance imaging","year":2016,"lang":"en","type":"article","venue":"Journal of Cerebral Blood Flow & Metabolism","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute on Drug Abuse","keywords":"Functional magnetic resonance imaging; Blood flow; Resting state fMRI; Magnetic resonance imaging; Cerebral blood flow; Perfusion; Blood-oxygen-level dependent; Blood oxygenation; Neuroscience; Nuclear magnetic resonance; Hemodynamics; Functional imaging; Perfusion scanning; Medicine; Psychology; Cardiology; Physics; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003091219,0.0004215243,0.0003063351,0.001074834,0.0001665402,0.0004021309,0.0002272857,0.0004559683,0.002327606],"category_scores_gemma":[0.001970662,0.0001741243,0.0001965333,0.0007891651,0.0002440224,0.0004929227,0.000171359,0.0002824264,0.0002781519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001598185,"about_ca_system_score_gemma":0.0002752536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001331199,"about_ca_topic_score_gemma":0.002015998,"domain_scores_codex":[0.9999226,0.00001262222,0.000006656649,0.00002342906,0.00002001604,0.0000146271],"domain_scores_gemma":[0.999719,0.0001240194,0.0000303442,0.00003128421,0.00007927614,0.00001603211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000686986,0.00007510656,0.01042814,0.0003312271,0.00008519337,0.0004656842,0.0002330991,0.002804767,0.8552405,0.0007685375,0.0007789748,0.1281018],"study_design_scores_gemma":[0.0001167972,0.001748446,0.5318244,0.00007966431,0.0006173098,0.004419127,0.000283623,0.07277022,0.3775364,0.004422536,0.006016651,0.0001649366],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.896607,0.0008298539,0.09636728,0.0001439073,0.00005996592,0.0001572181,0.002278085,0.0004352986,0.003121349],"genre_scores_gemma":[0.9594077,0.0005574716,0.03737585,0.00004482378,0.00005052883,0.0001474002,0.001584535,0.00007853273,0.0007530738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002327606,"threshold_uncertainty_score":0.007786691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494924130328142,"score_gpt":0.2165542102347233,"score_spread":0.2016049689314419,"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."}}