{"id":"W2161909632","doi":"10.1016/j.exger.2006.10.001","title":"A cross-national study of transitions in deficit counts in two birth cohorts: Implications for modeling ageing","year":2006,"lang":"en","type":"article","venue":"Experimental Gerontology","topic":"Global Health Care Issues","field":"Health Professions","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Canadian Institutes of Health Research; Vetenskapsrådet","keywords":"Cohort; Poisson distribution; Cohort effect; Demography; Markov chain; Gerontology; Cohort study; Medicine; Poisson regression; Ageing; Statistics; Mathematics; Environmental health; Population; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002541962,0.00009011024,0.0002377722,0.0001388162,0.0001606069,0.000002769939,0.00008736474,0.00007848746,0.0001204494],"category_scores_gemma":[0.00002408427,0.0001004818,0.00002667454,0.0001365194,0.00003421611,0.00006693916,0.00002152341,0.0001378254,0.00002264729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004390661,"about_ca_system_score_gemma":0.0001407167,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00554926,"about_ca_topic_score_gemma":0.02362697,"domain_scores_codex":[0.9986251,0.0001318938,0.00060881,0.0002186706,0.0001038686,0.0003116732],"domain_scores_gemma":[0.9995382,0.0001062956,0.00009160426,0.0001158392,0.0001178254,0.00003024934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002983033,0.003003419,0.898033,0.0001320551,0.00002036192,0.000009720507,0.02046369,0.01196769,0.002941828,0.06182525,0.001161522,0.000143194],"study_design_scores_gemma":[0.01350884,0.0005859251,0.9054909,0.0002078933,0.00001578023,0.000009876736,0.0472201,0.02317994,0.0001144743,0.007484406,0.001704979,0.0004768476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881005,0.0006456103,0.0006263145,0.0002095221,0.0002094777,0.00136409,0.00008754386,0.00002916366,0.008727719],"genre_scores_gemma":[0.9981185,0.000001517799,0.0006112732,0.0001192924,0.00005827174,0.000913246,0.00005360826,0.00001307799,0.0001112174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05434084,"threshold_uncertainty_score":0.9941893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1295874262739005,"score_gpt":0.5311818453724003,"score_spread":0.4015944190984999,"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."}}