{"id":"W2147765990","doi":"10.1142/s0217590810004048","title":"AN ACTUARIAL APPROACH TO ASSESSING PERSONAL INJURY COMPENSATIONS IN SINGAPORE: THEORY AND PRACTICE","year":2010,"lang":"en","type":"article","venue":"The Singapore Economic Review","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Personal injury; Lump sum; Actuarial science; Discounting; Plaintiff; Payment; Economics; Human settlement; Value (mathematics); Law; Finance; Engineering; Computer science; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01533991,0.0001875709,0.0003706301,0.0001131396,0.0007714898,0.0004285741,0.000486733,0.00008415023,0.0001289137],"category_scores_gemma":[0.0009712828,0.0001550188,0.00009352294,0.0002575645,0.0005366668,0.0009336182,0.00008510769,0.000441342,0.0000441712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001165039,"about_ca_system_score_gemma":0.0002295529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009630964,"about_ca_topic_score_gemma":0.00111699,"domain_scores_codex":[0.9966649,0.001855281,0.0004807528,0.0004355817,0.0002067227,0.0003568022],"domain_scores_gemma":[0.9982918,0.000727942,0.0003166156,0.0004637663,0.00005583943,0.0001440654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009047407,0.0004042331,0.00538182,0.0003754093,0.0001078278,0.000005726202,0.02280615,0.00003027456,0.0001774986,0.7699898,0.002921075,0.1977097],"study_design_scores_gemma":[0.0008454696,0.0001259861,0.08079637,0.001135482,0.0006316851,0.00005503394,0.02871656,0.000727122,0.00001311229,0.02957356,0.8558758,0.00150379],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7786367,0.004738655,0.0004627001,0.01269654,0.001755022,0.003025153,0.00002470863,0.0001301714,0.1985304],"genre_scores_gemma":[0.9888179,0.00385405,0.002917729,0.003651653,0.0005835785,0.00006736383,0.00001573434,0.00002374095,0.00006827088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8529547,"threshold_uncertainty_score":0.6321481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03373630248001568,"score_gpt":0.3850482502827395,"score_spread":0.3513119478027238,"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."}}