{"id":"W1525443459","doi":"10.1002/hbm.22441","title":"Gray matter alterations in early aging: A diffusion magnetic resonance imaging study","year":2013,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; National Institutes of Health","keywords":"Diffusion MRI; Gray (unit); Neuroscience; Magnetic resonance imaging; Neuroimaging; Precuneus; Context (archaeology); Psychology; Hum; Brain aging; Anatomy; Functional magnetic resonance imaging; Biology; Medicine; Nuclear medicine; Cognition; 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":[0.0005343497,0.0002936473,0.0002559137,0.001021241,0.0002267472,0.0002539742,0.0001507161,0.0002632232,0.0004613764],"category_scores_gemma":[0.0005462791,0.0001044979,0.0001360122,0.0004582699,0.0003473286,0.0002949568,0.0002712912,0.0001791142,0.0001333513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001586318,"about_ca_system_score_gemma":0.0002068045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001437824,"about_ca_topic_score_gemma":0.001432284,"domain_scores_codex":[0.9999214,0.00001544457,0.000008090735,0.00002910794,0.0000149135,0.00001102528],"domain_scores_gemma":[0.9997844,0.00002684337,0.00008368005,0.00002086174,0.00003258087,0.00005174115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001023036,0.0003537538,0.8808863,0.000237495,0.0002650395,0.004158956,0.00160573,0.0003797919,0.07184869,0.0005970486,0.0003930262,0.03825115],"study_design_scores_gemma":[0.000009816024,0.0004805676,0.9928218,0.00001216624,0.0000699079,0.003099993,0.0001923778,0.000289154,0.001904493,0.0003121754,0.0008010033,0.000006642311],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995887,0.002903301,0.0006012439,0.0000472838,0.000006416411,0.0000143184,0.00007294657,0.000006397642,0.0004611189],"genre_scores_gemma":[0.9985366,0.0007460933,0.0004156791,0.00001993571,0.00001943031,0.000005362279,0.00005438457,0.000001703127,0.0002008107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001437824,"threshold_uncertainty_score":0.002858937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04402403207313782,"score_gpt":0.3263843318786671,"score_spread":0.2823602998055293,"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."}}