{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001293969,0.0001552254,0.0001896598,0.0002462837,0.0002404518,0.00008087858,0.0001282508,0.00002159306,0.0002630557],"category_scores_gemma":[0.00002771616,0.0001526842,0.00004251075,0.0002934029,0.00005666571,0.0001751125,0.00009662216,0.0002645343,0.0001342738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005260584,"about_ca_system_score_gemma":0.00001028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002941775,"about_ca_topic_score_gemma":0.00001965357,"domain_scores_codex":[0.9987942,0.0000509017,0.0003224698,0.0003955105,0.0001539991,0.0002828965],"domain_scores_gemma":[0.999284,0.00005694225,0.00005993086,0.0004735131,0.00005312657,0.00007246473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002828118,0.0003093313,0.8984075,0.00002052879,0.000001614894,0.00003066849,0.002030297,0.00000166584,0.08818134,0.0005192979,0.006776864,0.003718058],"study_design_scores_gemma":[0.000709122,0.00006312666,0.9877558,0.0001737369,0.000006330996,0.00001923803,0.0003518926,0.0006261095,0.00003359009,0.003119821,0.006991188,0.0001500457],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816332,0.0001703341,0.00455835,0.009380003,0.00001940355,0.001557681,0.000001263459,0.000220228,0.002459553],"genre_scores_gemma":[0.9901766,0.000003083595,0.00370933,0.003073822,0.00007253242,0.0005100329,0.000008821489,0.00003577917,0.002410032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08934829,"threshold_uncertainty_score":0.6226282,"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."}}