{"id":"W2914891325","doi":"10.3386/w25030","title":"Understanding Joint Retirement","year":2018,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; HEC Montréal","funders":"National Institute on Aging; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Joint (building); Economics; Engineering; Structural engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004044586,0.0003581014,0.0006294625,0.001295205,0.0009902224,0.002341243,0.0009450646,0.001519988,0.006621406],"category_scores_gemma":[0.0154225,0.0005003471,0.0008601636,0.00119789,0.001834978,0.006635497,0.002869593,0.001133229,0.0006248581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001056251,"about_ca_system_score_gemma":0.0009504673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005909366,"about_ca_topic_score_gemma":0.003405194,"domain_scores_codex":[0.9985262,0.0007483334,0.00007313793,0.0003091471,0.0001950516,0.000148129],"domain_scores_gemma":[0.9939582,0.003212101,0.0008123376,0.001247493,0.0005190207,0.0002508609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008220302,0.00006397119,0.02887044,0.0001394768,0.0001053166,0.0002819251,0.005576231,0.02211392,0.0003183101,0.8781366,0.003832824,0.06047881],"study_design_scores_gemma":[0.00001305048,0.00003709831,0.01320511,0.00009211742,0.00004591466,0.0002598288,0.002036372,0.05855618,0.0001717101,0.9104789,0.01507887,0.00002477295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3916334,0.005681312,0.4984673,0.01228181,0.0002507303,0.00009612671,0.001097939,0.0001901228,0.09030122],"genre_scores_gemma":[0.9754402,0.001267631,0.01857156,0.0002743988,0.0001339538,0.00005026443,0.0003865298,0.00003015456,0.003845365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006621406,"threshold_uncertainty_score":0.02215087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9031720704027066,"score_gpt":0.6262882279433254,"score_spread":0.2768838424593812,"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."}}