{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001517665,0.00042011,0.0004731395,0.0001343062,0.0009020399,0.0001961854,0.000838495,0.0002918064,0.006175124],"category_scores_gemma":[0.0005458318,0.0003144097,0.0003152883,0.0004682736,0.0009837986,0.0005759853,0.0002508218,0.0001996796,0.002930088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000722217,"about_ca_system_score_gemma":0.0002776266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002660681,"about_ca_topic_score_gemma":0.003218861,"domain_scores_codex":[0.9952252,0.0005282125,0.0006596004,0.001057489,0.00124807,0.001281445],"domain_scores_gemma":[0.9976151,0.00028595,0.0002531343,0.001027597,0.0002153439,0.0006028674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006181491,0.001817216,0.5734766,0.0002418663,0.0003545203,0.0001097997,0.04569704,0.000007899272,0.01829954,0.05176434,0.1599812,0.1476318],"study_design_scores_gemma":[0.001681003,0.0003531067,0.03408129,0.0002932239,0.0001060231,0.000006531759,0.004775797,0.000004207337,0.01003035,0.01619673,0.9313387,0.001133046],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8264342,0.0003545538,0.0001944293,0.01789286,0.002365702,0.001297812,0.00005399248,0.0005901227,0.1508163],"genre_scores_gemma":[0.9609441,0.001008651,0.0002684177,0.0007058044,0.0008251933,0.0001685538,0.00001309014,0.00006501108,0.03600116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7713574,"threshold_uncertainty_score":0.9999308,"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."}}