{"id":"W3085997463","doi":"10.1002/hbm.25192","title":"Standard‐space atlas of the viscoelastic properties of the human brain","year":2020,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Elasticity and Material Modeling","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"NIH Office of the Director; National Institute on Aging; National Institute of Mental Health; Medical Research Council; Intelligence Advanced Research Projects Activity; National Cancer Institute; National Institute of Neurological Disorders and Stroke; University of Edinburgh; Office of the Director of National Intelligence; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; University of Illinois at Urbana-Champaign; Medical Research Council Canada","keywords":"Magnetic resonance elastography; Neuroanatomy; White matter; Viscoelasticity; Neuroimaging; Brain atlas; Human brain; Computer science; Atlas (anatomy); Neuroscience; Brain tissue; Magnetic resonance imaging; Brain mapping; Artificial intelligence; Elastography; Biomedical engineering; Biology; Anatomy; Medicine; Physics; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009337357,0.0005120577,0.0004784135,0.001883075,0.0005017251,0.001315859,0.0008379071,0.0006169257,0.006497269],"category_scores_gemma":[0.002252949,0.0003845864,0.0006371624,0.00206617,0.0005459212,0.0008169817,0.0008493731,0.0005911505,0.002252442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005501536,"about_ca_system_score_gemma":0.001764576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003287636,"about_ca_topic_score_gemma":0.006692322,"domain_scores_codex":[0.9995502,0.00009207882,0.00005986508,0.0001453319,0.0001205534,0.00003195905],"domain_scores_gemma":[0.9992532,0.0001671102,0.0001070211,0.0002030399,0.0002373376,0.00003237405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001118443,0.00033006,0.03514724,0.001595072,0.0006330583,0.001771022,0.003101019,0.05100698,0.165429,0.0727736,0.07477265,0.5923219],"study_design_scores_gemma":[0.0002636009,0.001457287,0.2728433,0.0004712352,0.0006221432,0.01248139,0.001545027,0.1840203,0.07490161,0.08551425,0.3654155,0.0004644505],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1511321,0.0009402517,0.8108807,0.0003519582,0.000144535,0.0009097967,0.01575298,0.003166457,0.01672127],"genre_scores_gemma":[0.4758582,0.001375817,0.484329,0.0002390884,0.0000929622,0.002838474,0.02498129,0.001249794,0.009035362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006497269,"threshold_uncertainty_score":0.02173549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03397940345919746,"score_gpt":0.2140527599264101,"score_spread":0.1800733564672127,"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."}}