{"id":"W3189685564","doi":"10.1101/2021.08.04.455143","title":"The genetic etiology of longitudinal measures of predicted brain ageing in a population-based sample of mid to late-age males","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health; Temple University; U.S. Department of Veterans Affairs; McGill University; U.S. Department of Defense","keywords":"Heritability; Etiology; Ageing; Twin study; Demography; Genetic correlation; Multivariate statistics; Longitudinal study; Longitudinal sample; Multivariate analysis; Biology; Population; Genetic model; Genetic variation; Developmental psychology; Psychology; Genetics; Medicine; Internal medicine; Statistics; Pathology; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008412051,0.0001897404,0.0002018885,0.0004643664,0.0003090636,0.0002885049,0.0001886974,0.0002043866,0.001225105],"category_scores_gemma":[0.002136267,0.0001509737,0.0002605332,0.0005010458,0.000262904,0.0001292397,0.0003140835,0.00027621,0.0001179365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001630481,"about_ca_system_score_gemma":0.0002461624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007447562,"about_ca_topic_score_gemma":0.009049365,"domain_scores_codex":[0.9997075,0.0001064932,0.00002079429,0.0001008736,0.00003225284,0.00003202725],"domain_scores_gemma":[0.999128,0.000259763,0.0002858843,0.0001683092,0.00007424576,0.00008389567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001366624,0.00001865847,0.9957389,0.000005660725,0.0001328608,0.00008566026,0.000225181,0.00007019326,0.0009920251,0.00008056696,0.0000536553,0.002460042],"study_design_scores_gemma":[0.000002076076,0.0000249144,0.9993809,0.000002256243,0.0000219865,0.0001010727,0.00007983617,0.0001704224,0.0001004333,0.00006184158,0.00005287674,0.000001321523],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992519,0.00009864391,0.0002827068,0.00002182428,0.000001859171,0.000002741172,0.0002281175,0.000002720263,0.0001095137],"genre_scores_gemma":[0.9994627,0.00004293779,0.0001820057,0.000005181611,0.000002385657,0.000004320184,0.0001733854,0.000002305826,0.0001248672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007447562,"threshold_uncertainty_score":0.01480842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341590007464723,"score_gpt":0.240249661145064,"score_spread":0.2168337610704168,"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."}}