{"id":"W2109144923","doi":"10.25336/p6js65","title":"Cohort Working Life Tables for Older Canadians","year":2010,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Human Resources and Skills Development Canada","keywords":"Life expectancy; Cohort; Life table; Demography; Table (database); Gerontology; Cohort effect; Statistics; Psychology; Medicine; Population; Sociology; Mathematics; Database; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.002235631,0.000526659,0.0004681951,0.009568568,0.001705256,0.001641073,0.001309659,0.0003365743,0.04715965],"category_scores_gemma":[0.01258392,0.0003475634,0.0009283384,0.01378676,0.0001899716,0.000659143,0.0006870083,0.0008058118,0.005823572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01250956,"about_ca_system_score_gemma":0.0311704,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9518409,"about_ca_topic_score_gemma":0.9547254,"domain_scores_codex":[0.9985508,0.0001193824,0.0001838908,0.0001474085,0.0007130194,0.0002854609],"domain_scores_gemma":[0.989931,0.0009219728,0.0006808391,0.0008691458,0.007111702,0.0004853738],"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":[0.0001233802,0.00003623288,0.02922789,0.0006607238,0.0001282083,0.0001071557,0.0005469255,0.002689073,0.0001529767,0.01339832,0.848801,0.1041282],"study_design_scores_gemma":[0.0000509575,0.00002395073,0.1232138,0.0003576977,0.00006729509,0.0001573407,0.0004008181,0.001686048,0.0001223083,0.002252822,0.8715912,0.00007570074],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.006741155,0.00125295,0.005062247,0.0005480952,0.0001986975,0.0007192513,0.9481031,0.0009311576,0.0364432],"genre_scores_gemma":[0.0503524,0.003119905,0.02375122,0.0004304448,0.00009740033,0.00149843,0.8836128,0.0003698864,0.03676756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04815912,"threshold_uncertainty_score":0.1577647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04560246168035859,"score_gpt":0.3467427472892173,"score_spread":0.3011402856088587,"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."}}