{"id":"W2078511436","doi":"10.1002/cjs.5550360202","title":"Forecasting mortality rates via density ratio modeling","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Curtin University of Technology","keywords":"Econometrics; Semiparametric model; Series (stratigraphy); Conditional probability distribution; Statistics; Moment (physics); Extension (predicate logic); Computer science; Mathematics; Nonparametric statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001187318,0.0001259888,0.0002488343,0.0002332746,0.001230602,0.00009754697,0.0002816953,0.00006808314,0.0001044064],"category_scores_gemma":[0.0005615926,0.0001355547,0.00008544571,0.0002946752,0.000404006,0.0002678292,0.00001107441,0.0002539747,0.000007327637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002567312,"about_ca_system_score_gemma":0.001606482,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1079322,"about_ca_topic_score_gemma":0.5114579,"domain_scores_codex":[0.9982041,0.00017436,0.0005316802,0.0001330677,0.0004858704,0.0004709231],"domain_scores_gemma":[0.9980326,0.00009526325,0.0003189365,0.000140216,0.0007734027,0.000639529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001383715,0.00003748157,0.9115354,0.00004774193,0.0002358763,0.002975718,0.01726742,0.01354048,0.000008920342,0.03688994,0.01066232,0.006784817],"study_design_scores_gemma":[0.001776378,0.0003128195,0.4927203,0.0002516883,0.0006100244,0.000418039,0.01332116,0.3569244,0.00008658495,0.1158172,0.0160171,0.001744366],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7380115,0.0001917156,0.2577238,0.0001661185,0.0009264838,0.0001535524,0.00008836909,0.00001006308,0.002728417],"genre_scores_gemma":[0.987641,0.0001397419,0.01154146,0.0001745753,0.000365492,0.000001164583,0.00000638436,0.00001373252,0.0001164773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4188151,"threshold_uncertainty_score":0.946492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09095401882357076,"score_gpt":0.2946302585977734,"score_spread":0.2036762397742026,"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."}}