{"id":"W2790446590","doi":"10.1093/cercor/bhy032","title":"Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms","year":2018,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute of Mental Health; Institut National de la Santé et de la Recherche Médicale; National Institutes of Health; International Mental Health Research Organization","keywords":"Generalizability theory; Reliability (semiconductor); Computer science; Neuroimaging; Sample size determination; Cognition; Artificial intelligence; Machine learning; Data mining; Psychology; Neuroscience; Power (physics); Statistics; Mathematics","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":[0.06059572,0.0007039131,0.0007552041,0.002373949,0.001161907,0.002456183,0.001406241,0.0008559459,0.002149823],"category_scores_gemma":[0.1761791,0.000592031,0.00120965,0.001962774,0.003284149,0.002956571,0.002749031,0.001388505,0.0003419368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005382975,"about_ca_system_score_gemma":0.0008552978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00163823,"about_ca_topic_score_gemma":0.002838554,"domain_scores_codex":[0.9810894,0.01295482,0.0008761172,0.003149524,0.001563872,0.0003662398],"domain_scores_gemma":[0.8473153,0.08497949,0.01467211,0.04495578,0.006864931,0.001212533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001928597,0.0006147426,0.5692825,0.001874512,0.007391158,0.001210804,0.0133464,0.04145192,0.0925265,0.03650292,0.007683023,0.2261868],"study_design_scores_gemma":[0.0002253431,0.001210442,0.7054367,0.0003170875,0.001653242,0.001589455,0.002221918,0.0810718,0.02434367,0.1685237,0.01316025,0.0002464792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4125232,0.0006392153,0.5784187,0.0009133522,0.0002757635,0.000947534,0.001083281,0.001617181,0.00358178],"genre_scores_gemma":[0.9108676,0.0001207336,0.08616699,0.0002340605,0.00009905333,0.0009464314,0.0008947533,0.0003280282,0.000342357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06059572,"threshold_uncertainty_score":0.3204646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.114046370668454,"score_gpt":0.3487424549112227,"score_spread":0.2346960842427687,"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."}}