{"id":"W3179371357","doi":"10.1002/mrm.28926","title":"MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI","year":2021,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Connectomics; Diffusion MRI; Fractional anisotropy; Connectome; Pattern recognition (psychology); Artificial intelligence; Orientation (vector space); Data set; Computer science; Nuclear magnetic resonance; Mathematics; Statistics; Medicine; Psychology; Magnetic resonance imaging; Neuroscience; Physics; Functional connectivity; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005092121,0.0002054665,0.0004994865,0.0001834752,0.00009074025,0.000008950611,0.00009351717,0.00009357313,0.00009812321],"category_scores_gemma":[0.000800203,0.0001816405,0.00004039779,0.0006417821,0.0002060887,0.00005880378,0.0000997678,0.0003033337,0.000001220192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009236154,"about_ca_system_score_gemma":0.00004232304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001660598,"about_ca_topic_score_gemma":0.0001487801,"domain_scores_codex":[0.9981232,0.0001107984,0.0005369683,0.0006735318,0.000216843,0.0003386048],"domain_scores_gemma":[0.9984231,0.0007276487,0.00007064892,0.0005224854,0.0001401035,0.0001160213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003948779,0.001215161,0.5225175,0.0003883163,0.000006276055,0.0004079151,0.001117977,0.00001107272,0.06075442,0.00156722,0.001230383,0.4103889],"study_design_scores_gemma":[0.009090682,0.0004586455,0.9196019,0.001149404,0.00005103287,0.0001437068,0.0003345424,0.03542721,0.001446288,0.004482268,0.02756255,0.0002517394],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9373217,0.01018588,0.03383418,0.01452356,0.0001232745,0.002985616,0.00004303944,0.0001654549,0.00081731],"genre_scores_gemma":[0.8563749,0.003414456,0.1373222,0.001058821,0.0001369317,0.0007940937,0.00007880875,0.00004283166,0.0007769924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4101371,"threshold_uncertainty_score":0.7407086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04634699271568218,"score_gpt":0.3551013306261533,"score_spread":0.3087543379104711,"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."}}