{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007564959,0.0001819794,0.0002679997,0.001362979,0.0007586877,0.001166707,0.0004164484,0.001092919,0.0132595],"category_scores_gemma":[0.004927647,0.0002116041,0.0006381545,0.001898595,0.0002572715,0.0009949853,0.001265725,0.001355996,0.00227736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006558239,"about_ca_system_score_gemma":0.0008738767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03344817,"about_ca_topic_score_gemma":0.05798917,"domain_scores_codex":[0.9994978,0.0001049043,0.00004284968,0.00004709465,0.00006451863,0.0002428058],"domain_scores_gemma":[0.9962695,0.0003592822,0.001466187,0.0001449596,0.0003851725,0.001375008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001156285,0.00009450535,0.9919543,0.00001995959,0.00004774841,0.00005956889,0.0002389293,0.00007817715,0.00002866366,0.0002902886,0.001756448,0.005315744],"study_design_scores_gemma":[0.000003679555,0.00005870978,0.9954361,0.00003760218,0.00003119596,0.00006861374,0.0007898445,0.00006807558,0.00001630923,0.0001507378,0.003334876,0.000004352859],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793352,0.002339349,0.000067557,0.004114935,0.000129377,0.000007697257,0.005327051,0.000009943602,0.00866891],"genre_scores_gemma":[0.9860596,0.001127668,0.00004329107,0.0003909843,0.0001344378,0.00001138157,0.003724648,0.000006497307,0.008501414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03344817,"threshold_uncertainty_score":0.06650692,"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."}}