{"id":"W6989450306","doi":"","title":"ASSET LIABILITY MANAGEMENT AND JOINT MORTALITY MODELLING IN OLD-AGE INSURANCE","year":2016,"lang":"en","type":"dissertation","venue":"IRIS","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Actuary; Pension; Liability; Asset (computer security); Context (archaeology); Asset management; Population; Asset allocation","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":[0.002772247,0.0004257735,0.0006540178,0.0004174195,0.0004243539,0.0002053437,0.0004306225,0.0003878578,0.00009258819],"category_scores_gemma":[0.00006684431,0.0004130725,0.0002229108,0.0005483608,0.0002959883,0.0003496038,0.0000876274,0.000397744,0.00004081396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002892373,"about_ca_system_score_gemma":0.00006770498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0129708,"about_ca_topic_score_gemma":0.02421037,"domain_scores_codex":[0.9958843,0.0004476695,0.0008665461,0.001016931,0.001081149,0.0007033896],"domain_scores_gemma":[0.9984793,0.00007497122,0.0004226642,0.000725971,0.0001382255,0.0001588338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001176315,0.0005679199,0.9094604,0.002235944,0.0004506567,0.0002956594,0.0176849,0.0003563927,0.00002404669,0.03803588,0.001883483,0.02888704],"study_design_scores_gemma":[0.000431179,0.0000171151,0.9596061,0.0004130712,0.00008188649,1.155598e-7,0.002473825,0.00008684152,0.0000165725,0.0227416,0.01355126,0.0005804794],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8509067,0.0006691819,0.0003058611,0.0001385624,0.001122005,0.001422487,0.00009886105,0.0001275715,0.1452088],"genre_scores_gemma":[0.9833406,0.004705453,0.000650882,0.00008991137,0.00019119,0.0002435677,0.0001320749,0.00004485855,0.01060149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1346073,"threshold_uncertainty_score":0.9998321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0420426593483902,"score_gpt":0.3240967646950942,"score_spread":0.2820541053467041,"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."}}