{"id":"W2019742604","doi":"10.1287/inte.30.1.96.11617","title":"An Asset and Liability Management System for Towers Perrin-Tillinghast","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian General-Tower (Canada)","funders":"","keywords":"Liability; Asset (computer security); Pension; Actuarial science; Business; Plan (archaeology); Investment (military); Asset management; Generator (circuit theory); Finance; Risk management; Risk analysis (engineering); Computer science; Power (physics); Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001691817,0.0004747613,0.0003851595,0.001223909,0.0005200439,0.001567327,0.0009458989,0.0005714619,0.04265499],"category_scores_gemma":[0.004144773,0.0004429068,0.0003226991,0.0007464837,0.0002158772,0.001901516,0.001232546,0.0007279089,0.009523398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065583,"about_ca_system_score_gemma":0.001810023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004324331,"about_ca_topic_score_gemma":0.003313186,"domain_scores_codex":[0.9991969,0.0001695014,0.00006451656,0.0002040668,0.0003151823,0.00004975536],"domain_scores_gemma":[0.9981843,0.0005725912,0.0001843858,0.0003909733,0.0004472198,0.0002205682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009975524,0.0004727241,0.01268138,0.000235133,0.00008733957,0.0007928562,0.0007101541,0.07875156,0.01484738,0.02995961,0.2218664,0.6385978],"study_design_scores_gemma":[0.0002499284,0.0002448428,0.00564167,0.00009332207,0.00006498599,0.000564884,0.0001113766,0.6625572,0.01196315,0.01115565,0.307228,0.0001249835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08327718,0.0007223595,0.6287456,0.001859527,0.0002557444,0.0009518391,0.008246257,0.1912976,0.08464391],"genre_scores_gemma":[0.5690908,0.0008413575,0.3118722,0.0004474004,0.0002517398,0.0008511579,0.01736378,0.005913238,0.09336837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04265499,"threshold_uncertainty_score":0.1426952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01581097410358162,"score_gpt":0.296733585176161,"score_spread":0.2809226110725794,"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."}}