{"id":"W2537264758","doi":"10.1556/650.2016.30510","title":"Nyugdíjas orvosok helyzete Magyarországon – országos, reprezentatív felmérés eredményei alapján","year":2016,"lang":"en","type":"article","venue":"Orvosi Hetilap","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Demography; Gerontology; Epidemiology; Medicine; Quality of life (healthcare); Population; Population ageing; Psychology; Geography; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004941294,0.0003664451,0.0003833396,0.0005831726,0.001038758,0.001222096,0.0002692113,0.0004941134,0.02446039],"category_scores_gemma":[0.0006021638,0.0001312489,0.0002268923,0.0005635075,0.0004587686,0.0006035385,0.001266264,0.0007930272,0.004858462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005610267,"about_ca_system_score_gemma":0.00189488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005268379,"about_ca_topic_score_gemma":0.007063942,"domain_scores_codex":[0.9997327,0.00005479576,0.00001764565,0.00004171768,0.00006489,0.00008838946],"domain_scores_gemma":[0.999866,0.00003226943,0.00002603085,0.000009940863,0.00002834753,0.00003739462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001338194,0.0005193467,0.04397878,0.003294996,0.000163816,0.009707761,0.009497882,0.0008265402,0.009639428,0.02864464,0.09712388,0.7952647],"study_design_scores_gemma":[0.0000648007,0.0002187262,0.0928425,0.001142155,0.00006899868,0.003937809,0.0100211,0.0002680106,0.002153647,0.00449342,0.8847415,0.00004728679],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6554295,0.1433233,0.003308548,0.02281675,0.004934419,0.0001075953,0.002959989,0.0003880377,0.1667318],"genre_scores_gemma":[0.800376,0.07628395,0.003965486,0.00173496,0.0008209717,0.0000889463,0.001882333,0.0001284632,0.1147189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02446039,"threshold_uncertainty_score":0.08182824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1984945216596826,"score_gpt":0.4280078633029155,"score_spread":0.2295133416432329,"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."}}