{"id":"W818453381","doi":"10.1080/10920277.2001.10595979","title":"A Macro-Economic Indicator of Age at Retirement","year":2001,"lang":"en","type":"article","venue":"North American Actuarial Journal","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Macro; Economics; Retirement age; Econometrics; Computer science; Finance; Pension","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007889546,0.000160625,0.0003635397,0.0001081135,0.0005943909,0.00009385405,0.0004633059,0.00004300209,0.002485194],"category_scores_gemma":[0.0001155847,0.0001481033,0.0002086607,0.0002160143,0.001158007,0.0001507964,0.00008717577,0.0002130872,0.0001210197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059088,"about_ca_system_score_gemma":0.0004286499,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005777598,"about_ca_topic_score_gemma":0.02176438,"domain_scores_codex":[0.9976946,0.0002855571,0.000580402,0.0002496994,0.0006422528,0.0005474925],"domain_scores_gemma":[0.9985089,0.00008206614,0.0006768073,0.0002365237,0.0000493006,0.0004463843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002081711,0.0001540165,0.9522598,0.000005019975,0.00008078096,0.00002626859,0.00688673,0.00002161797,0.0001079043,0.000237015,0.004100216,0.03591248],"study_design_scores_gemma":[0.001266579,0.0006717001,0.7774934,0.00001801891,0.00009057625,0.00001679544,0.004748084,0.000008467141,0.0001245669,0.0004638344,0.2146808,0.0004171361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841709,0.00002571187,0.00005325474,0.001152187,0.0008584107,0.0002452483,0.00001125567,0.0000265193,0.01345649],"genre_scores_gemma":[0.9971565,0.0006806431,0.0001937952,0.0002465601,0.001282337,0.000008187681,0.000005616759,0.00001527765,0.0004110663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2105806,"threshold_uncertainty_score":0.9984267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070466023159736,"score_gpt":0.3885075312047012,"score_spread":0.2814609288887275,"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."}}