{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01229938,0.0001736582,0.0003481091,0.0004533867,0.0006149047,0.0001965416,0.0007567461,0.00034844,0.002743161],"category_scores_gemma":[0.001070209,0.0001883937,0.0002086842,0.0001393934,0.001700715,0.0001215709,0.0007432111,0.0005774005,0.0002650296],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009424643,"about_ca_system_score_gemma":0.002781718,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006624342,"about_ca_topic_score_gemma":0.003069478,"domain_scores_codex":[0.99532,0.0006810323,0.0006827376,0.0006490846,0.002083248,0.0005838593],"domain_scores_gemma":[0.9978322,0.000486674,0.000262457,0.0004023174,0.0008183442,0.0001979758],"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":[0.0000325159,0.0001199316,0.003875185,0.0001376792,0.0001004388,4.737182e-7,0.002492574,0.0001968563,0.00003384405,0.936766,0.05614209,0.0001024197],"study_design_scores_gemma":[0.0002090856,0.00007504554,0.0006523046,0.0001379606,0.000007989038,8.999092e-8,0.002366757,0.0002389339,0.0001829235,0.9917302,0.004220902,0.0001778562],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03459326,0.0001062517,0.00007341227,0.008261592,0.001313392,0.001300262,0.00004551387,0.00003818908,0.9542681],"genre_scores_gemma":[0.9952533,0.0004542371,0.000214431,0.00003499085,0.001408972,0.000114276,0.00008103336,0.00002074406,0.002417979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9606601,"threshold_uncertainty_score":0.9999906,"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."}}