{"id":"W3124450192","doi":"","title":"Eliciting Subjective Survival Curves: Lessons from Partial Identification","year":2015,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Life expectancy; Rounding; Construct (python library); Consistency (knowledge bases); Econometrics; Point (geometry); Inference; Parametric statistics; Mathematics; Expectancy theory; Identification (biology); Statistics; Psychology; Computer science; Social psychology; Artificial intelligence; Medicine; Discrete mathematics","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"],"consensus_categories":[],"category_scores_codex":[0.01253196,0.0003286143,0.0006350128,0.0005281359,0.0006326089,0.0004948018,0.001437034,0.0004904608,0.0001136928],"category_scores_gemma":[0.002070682,0.000411072,0.0002646771,0.0003825456,0.0009892358,0.0002845491,0.001135448,0.001673842,0.0000540033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001908912,"about_ca_system_score_gemma":0.001341517,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02325034,"about_ca_topic_score_gemma":0.05066812,"domain_scores_codex":[0.9931956,0.002281189,0.0009413537,0.00137985,0.001088767,0.00111326],"domain_scores_gemma":[0.996667,0.0008010945,0.0004938668,0.00119119,0.0005405742,0.0003062696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002843559,0.000994624,0.33743,0.0007085829,0.001092951,0.000102519,0.03475811,0.008315533,0.00009273962,0.02690917,0.001802532,0.5875089],"study_design_scores_gemma":[0.002537256,0.0001361853,0.5647839,0.002767428,0.0002289267,0.000001080695,0.1088265,0.009109586,0.0002995641,0.1789396,0.1286979,0.003671957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7487239,0.0005861391,0.00002350014,0.00170927,0.002353241,0.001820973,0.000368237,0.0001324709,0.2442822],"genre_scores_gemma":[0.9694583,0.02766312,0.0001171251,0.0000750114,0.001131691,0.0004685826,0.0002728621,0.00005910403,0.0007542493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5838369,"threshold_uncertainty_score":0.9998341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09422028548523298,"score_gpt":0.4081399816353739,"score_spread":0.3139196961501409,"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."}}