{"id":"W3150581439","doi":"","title":"Health Shocks, Human Capital, and Labor Market Outcomes","year":2018,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Global Health Care Issues","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University; Institut für Arbeitsmarkt- und Berufsforschung; W.E. Upjohn Institute for Employment Research","keywords":"Human capital; Health care; Labour economics; Economics; Business; Demographic economics; Economic growth","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","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":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0040428,0.0002163805,0.0005832861,0.0003322999,0.001450095,0.00002932259,0.0003581791,0.000269532,0.001156361],"category_scores_gemma":[0.0007540888,0.0002085632,0.00004644267,0.0001935334,0.0005109502,0.00015,0.0004600993,0.001120464,0.0002297645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141337,"about_ca_system_score_gemma":0.0009670971,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002189043,"about_ca_topic_score_gemma":0.0187622,"domain_scores_codex":[0.9954017,0.001162928,0.0008831878,0.0006432878,0.0002642166,0.001644641],"domain_scores_gemma":[0.9971345,0.001126778,0.0001895937,0.0007227925,0.0002541732,0.0005721549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008353841,0.0000740956,0.9592949,0.0003307314,0.00002315761,0.000009392601,0.003696319,5.776853e-7,0.00001533266,0.004064174,0.01398104,0.01842671],"study_design_scores_gemma":[0.00096061,0.0003889843,0.8619974,0.00022381,0.000001628804,0.00000274811,0.006969451,0.00006276483,0.000004904914,0.0016922,0.1274979,0.0001976448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8766104,0.000303592,9.901962e-8,0.00703587,0.0005076294,0.001100831,0.00007803307,0.00007426654,0.1142893],"genre_scores_gemma":[0.9801891,0.00308935,0.0003188556,0.002974215,0.0004240676,0.0001496201,0.00001655632,0.00005493094,0.01278328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1135169,"threshold_uncertainty_score":0.9998499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06171374520099951,"score_gpt":0.4779743368453184,"score_spread":0.4162605916443189,"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."}}