{"id":"W2903903800","doi":"10.1101/497743","title":"Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut universitaire en santé mentale de Montréal; McGill University; Université Laval; Institut Universitaire en Santé Mentale de Québec; Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Resting state fMRI; Consistency (knowledge bases); Functional magnetic resonance imaging; Multivariate statistics; Neuroimaging; Sample (material); Computer science; Multivariate analysis; Artificial intelligence; Pattern recognition (psychology); Psychology; Statistics; Mathematics; Machine learning; Neuroscience; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002281403,0.0003145507,0.0003582387,0.001119071,0.0006299705,0.000648728,0.0006365587,0.000346951,0.0005577078],"category_scores_gemma":[0.005623172,0.0002544117,0.0003233874,0.00104143,0.0005504686,0.0003013274,0.0005040226,0.0003612543,0.0001304756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007504884,"about_ca_system_score_gemma":0.0007703435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07743067,"about_ca_topic_score_gemma":0.1723696,"domain_scores_codex":[0.9989257,0.0001841926,0.00007173902,0.0003907917,0.0003108546,0.0001167238],"domain_scores_gemma":[0.9966048,0.0007357481,0.0004520811,0.0006515048,0.001396635,0.0001591792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001221402,0.0001048223,0.8797085,0.0001134574,0.001291411,0.0005747512,0.001832601,0.004368905,0.06238352,0.0003224158,0.001698915,0.04637939],"study_design_scores_gemma":[0.000005757391,0.00006372435,0.9944728,0.000002884848,0.00006829978,0.0002323279,0.0001361422,0.001890703,0.00270373,0.00006573564,0.0003398045,0.00001815723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947309,0.0001182302,0.003822033,0.00002799881,0.000006678596,0.00002454666,0.0007492542,0.00008954161,0.0004308261],"genre_scores_gemma":[0.9972013,0.00002604164,0.001489132,0.000006398684,0.000003155747,0.0000112423,0.001058085,0.00002436651,0.0001802635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07743067,"threshold_uncertainty_score":0.1539599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04288270239271428,"score_gpt":0.2492988692939014,"score_spread":0.2064161669011872,"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."}}