{"id":"W2730768487","doi":"10.1093/geroni/igx004.2640","title":"THE CANADIAN LONGITUDINAL STUDY ON AGING (CLSA): A PLATFORM FOR RESEARCH ON AGING","year":2017,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Migration, Aging, and Tourism Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Dalhousie University","funders":"National Institute on Aging; National Institutes of Health","keywords":"Scope (computer science); Multidisciplinary approach; Healthy aging; Successful aging; Gerontology; Life course approach; Population; Longitudinal data; Longitudinal study; Population ageing; Psychology; Medicine; Developmental psychology; Computer science; Political science; Sociology; Demography; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.04728379,0.001734511,0.003543115,0.01877491,0.009132331,0.007878052,0.005230042,0.002728512,0.0172208],"category_scores_gemma":[0.09850868,0.00125158,0.001866454,0.02762351,0.002544525,0.003682532,0.0104028,0.006538261,0.005103776],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06226317,"about_ca_system_score_gemma":0.3198764,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9705026,"about_ca_topic_score_gemma":0.9820021,"domain_scores_codex":[0.9665854,0.00827151,0.00233055,0.001741157,0.01781421,0.00325725],"domain_scores_gemma":[0.8276919,0.02230439,0.005145075,0.008089382,0.114357,0.0224123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001592303,0.00006099817,0.01616742,0.001535119,0.0002127395,0.00006686457,0.00163042,0.0002072744,0.0001932596,0.009693107,0.8160372,0.1540364],"study_design_scores_gemma":[0.0001602822,0.00007876267,0.09984103,0.0047278,0.0001925189,0.00006244066,0.002139378,0.0004183136,0.0001507619,0.004261343,0.8877335,0.0002340483],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.009740221,0.1165028,0.0309935,0.1926526,0.02269423,0.008694073,0.4640259,0.004504984,0.1501916],"genre_scores_gemma":[0.1082838,0.1847313,0.2001129,0.04383778,0.005768116,0.02616558,0.3710237,0.002082383,0.05799442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9377368,"threshold_uncertainty_score":0.451753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3246896221343837,"score_gpt":0.4914464235600117,"score_spread":0.1667568014256279,"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."}}