{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007670727,0.001154275,0.001084882,0.004067434,0.00168501,0.006661746,0.00175167,0.001553621,0.7912202],"category_scores_gemma":[0.006295308,0.0003868684,0.0007123513,0.005179268,0.0004919827,0.005059972,0.002821259,0.001708277,0.7570123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002087624,"about_ca_system_score_gemma":0.001660957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003116798,"about_ca_topic_score_gemma":0.003691713,"domain_scores_codex":[0.9987636,0.000102413,0.0000951325,0.0002213762,0.0007122754,0.0001052842],"domain_scores_gemma":[0.9974956,0.0003108014,0.000116389,0.0004115842,0.001279106,0.0003864019],"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":[0.00001482697,0.00002164128,0.0001380542,0.0001387928,0.000002065064,0.00002857646,0.0000525572,0.00005398463,0.0001397816,0.004328799,0.901268,0.09381298],"study_design_scores_gemma":[0.000002110647,0.000007203789,0.0002196822,0.00007145156,0.00000107127,0.00004077591,0.00003675007,0.00003069394,0.00004802221,0.001415513,0.998123,0.000003758113],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0005294348,0.002349118,0.002421421,0.002982714,0.005793598,0.0002204896,0.01613048,0.002269553,0.9673032],"genre_scores_gemma":[0.001813776,0.001679202,0.001141225,0.0008871517,0.001080345,0.0001219285,0.01043381,0.0007449611,0.9820977],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2087798,"threshold_uncertainty_score":0,"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."}}