{"id":"W4230391408","doi":"10.1017/9781108784184.026","title":"Index","year":2019,"lang":"en","type":"paratext","venue":"Cambridge University Press eBooks","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Index (typography); Actuarial science; Life insurance; Cash flow; Computer science; Finance; Economics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004607946,0.0004061988,0.0005491884,0.0004086663,0.0007572976,0.0001874837,0.001520035,0.000644444,0.0001073157],"category_scores_gemma":[0.0000188518,0.0005153739,0.0003913655,0.0001115911,0.0007257176,0.0001964794,0.0005219888,0.0006764265,0.001898072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004421077,"about_ca_system_score_gemma":0.0004352599,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01663939,"about_ca_topic_score_gemma":0.0001833272,"domain_scores_codex":[0.9968541,0.0005308494,0.0002358502,0.0007651248,0.0008499796,0.0007640466],"domain_scores_gemma":[0.9982235,0.00008724767,0.0003471614,0.0008397027,0.0002607363,0.0002416372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004271594,0.00003179302,0.0002867139,0.000146293,0.0002026326,0.00006106633,0.000439832,0.00002703559,0.000001668592,0.1661379,0.8319083,0.0007139595],"study_design_scores_gemma":[0.0004795245,0.00002153141,0.0008084535,0.00009044549,0.0001189435,4.220303e-7,0.001051293,0.00001741035,0.00001371888,0.000001100096,0.9968222,0.000574943],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001651651,0.0002995291,0.0005126611,0.00004690153,0.005521026,0.001177479,0.0003947149,0.0001529945,0.990243],"genre_scores_gemma":[0.03658583,0.00090412,0.00002652803,0.000169482,0.0006145771,0.000003071721,0.00009833989,0.0000432335,0.9615548],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1661368,"threshold_uncertainty_score":0.9997298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02146431555924051,"score_gpt":0.2544993153435353,"score_spread":0.2330349997842948,"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."}}