{"id":"W2561763806","doi":"10.1017/s0269964816000504","title":"THE ANALYTIC APPROACH FOR THE STOCHASTIC PROJECTION OF THE PUBLIC PENSION FUND","year":2016,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pension fund; Valuation (finance); Moment (physics); Stochastic differential equation; Pension; Projection (relational algebra); Matching (statistics); Order (exchange); Actuarial science; Revenue; Econometrics; Economics; Mathematics; Computer science; Mathematical optimization; Applied mathematics; Finance; Statistics; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"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":[0.008874489,0.00006761259,0.00007039572,0.0000531051,0.001256304,0.0001790754,0.0006221118,0.00002568653,0.000002096993],"category_scores_gemma":[0.001288861,0.00002259041,0.00006292039,0.0006629791,0.0009802425,0.0004399604,0.00006210298,0.00006945283,5.083782e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004614102,"about_ca_system_score_gemma":0.00008646226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001366064,"about_ca_topic_score_gemma":0.0002546317,"domain_scores_codex":[0.9986638,0.0001149044,0.0002803377,0.0001075002,0.0006181666,0.0002152787],"domain_scores_gemma":[0.9984302,0.001131923,0.0001247643,0.0001906739,0.0001051408,0.00001728811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001579736,0.00006520413,0.0124097,0.00009448951,0.00003065347,1.209542e-8,0.01061637,0.124447,0.00001275042,0.8332717,0.0003397015,0.01869664],"study_design_scores_gemma":[0.0003551854,0.00008979364,0.1958585,0.00005645044,0.00003206699,0.000002975428,0.01083993,0.7421728,0.00001124287,0.03407871,0.01628275,0.0002196121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9368488,0.0003644806,0.0250817,0.02723961,0.0007476715,0.004257747,0.00001856575,0.0000546844,0.005386703],"genre_scores_gemma":[0.9995499,0.00003809445,0.0001536067,0.00004045776,0.00004559313,0.000142245,4.498206e-7,0.000001304964,0.00002835828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.799193,"threshold_uncertainty_score":0.96626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05019828892213434,"score_gpt":0.2825738307035427,"score_spread":0.2323755417814083,"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."}}