{"id":"W1481977482","doi":"10.4054/mpidr-wp-2008-013","title":"Beyond the Kannisto-Thatcher Database on Old Age Mortality: an assessment of data quality at advanced ages","year":2008,"lang":"en","type":"preprint","venue":"","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Max-Planck-Institut für demografische Forschung","keywords":"Data quality; Database; Quality (philosophy); Quality assessment; Demography; Medicine; Computer science; Engineering; Operations management; External quality assessment; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008090899,0.0006808185,0.001044255,0.0002108318,0.001128196,0.0002347096,0.005143382,0.0003992569,0.000465923],"category_scores_gemma":[0.000271871,0.0005365813,0.0003777366,0.0004981574,0.001973122,0.000792545,0.005089713,0.001060904,0.00002299848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004242696,"about_ca_system_score_gemma":0.0004270481,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05184373,"about_ca_topic_score_gemma":0.08637,"domain_scores_codex":[0.9901351,0.002479078,0.001316173,0.002077713,0.003157733,0.0008341999],"domain_scores_gemma":[0.9895025,0.0004154368,0.001225678,0.008267749,0.000299256,0.0002893147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002380967,0.005677246,0.5264556,0.001677711,0.003060236,0.0004727609,0.02080519,0.002498813,0.0003127293,0.3323574,0.07870141,0.02774277],"study_design_scores_gemma":[0.0008296522,0.0001174931,0.9055001,0.0001777365,0.0004577538,6.333429e-7,0.006400051,0.0003449234,0.00009103859,0.01020811,0.07440577,0.001466698],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7558923,0.000342284,0.0005847056,0.001302833,0.002004897,0.002890985,0.00337241,0.0003101591,0.2332994],"genre_scores_gemma":[0.9730918,0.005641886,0.006417082,0.001732185,0.0006209725,0.0002223714,0.005071404,0.00007713021,0.007125128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3790445,"threshold_uncertainty_score":0.9997086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1816645656465325,"score_gpt":0.4818200956570313,"score_spread":0.3001555300104989,"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."}}