{"id":"W3125482798","doi":"10.2139/ssrn.2918055","title":"Retirement Spending and Biological Age","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Global Health Care Issues","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Demographic economics; Economics; Gerontology; Demography; Business; Medicine; Sociology","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":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003346581,0.000134833,0.0002369802,0.00004984225,0.003418264,0.00004824267,0.000373156,0.0001699936,0.000145327],"category_scores_gemma":[0.0004847274,0.0001011453,0.00004857757,0.00002632704,0.0001075873,0.0001469096,0.000207846,0.00296194,0.0002667177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272821,"about_ca_system_score_gemma":0.000899264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003390732,"about_ca_topic_score_gemma":0.002202854,"domain_scores_codex":[0.9959364,0.000293861,0.0003808366,0.0002127708,0.0002099504,0.002966194],"domain_scores_gemma":[0.9989237,0.0001064804,0.0003950666,0.0003071328,0.00006558686,0.0002020823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001638427,0.00004160688,0.4857411,0.00005699123,0.00007956547,0.00008273635,0.0009313372,2.09258e-7,0.000332493,0.4935643,0.002539108,0.01646668],"study_design_scores_gemma":[0.001812279,0.0007829523,0.5526456,0.0003416242,0.00002856798,0.0001895444,0.008849384,0.00001119418,0.00001445416,0.381469,0.05355008,0.0003052773],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975161,0.00297279,0.0001615031,0.00374099,0.0009710801,0.0003250857,0.000002701369,0.00004459267,0.01662033],"genre_scores_gemma":[0.9868922,0.009472931,0.0001338631,0.0002925457,0.0007637264,0.000008007465,0.000001908256,0.00001405218,0.002420722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1120953,"threshold_uncertainty_score":0.9993383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1185378912703246,"score_gpt":0.4787723852594041,"score_spread":0.3602344939890795,"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."}}