{"id":"W1513707229","doi":"10.1553/populationyearbook2005s243","title":"Fertility in Austria: An Overview","year":2006,"lang":"en","type":"article","venue":"Vienna Yearbook of Population Research","topic":"Global Health Care Issues","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fertility; Productivity; Postponement; Geography; Population ageing; Population; Demography; Demographic economics; Political science; Sociology; Economics; Economic growth","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":[],"consensus_categories":[],"category_scores_codex":[0.004522847,0.0001005204,0.0002898415,0.0004131628,0.000254122,0.000006709992,0.0002319469,0.0002443168,0.000804829],"category_scores_gemma":[0.0004631841,0.00009677518,0.00003857405,0.000849702,0.00008021751,0.0001937826,0.0001263387,0.0007509037,0.0005130541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004928967,"about_ca_system_score_gemma":0.0002253644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07532527,"about_ca_topic_score_gemma":0.0191065,"domain_scores_codex":[0.9952027,0.002068313,0.0008510798,0.0003241285,0.0008328493,0.0007209043],"domain_scores_gemma":[0.9983305,0.0003802262,0.0001395976,0.0005142943,0.0004982661,0.0001371607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001312489,0.0002069295,0.9696237,0.000570032,0.000002344293,0.00000570287,0.0004984227,0.00004815467,0.0004449371,0.02376317,0.003163783,0.001541586],"study_design_scores_gemma":[0.0004851814,0.00008760668,0.9791555,0.0002936975,0.0000015934,9.250936e-8,0.0003711937,0.0003690078,0.00002487798,0.009629582,0.009509654,0.00007194681],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858982,0.00116283,0.000004397746,0.0004967244,0.0001832346,0.001179454,0.00001924771,0.00005056246,0.01100533],"genre_scores_gemma":[0.9977089,0.00004372029,0.0002847207,0.0000536304,0.0001485738,0.00007162162,0.00007899311,0.00001816301,0.00159165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05621876,"threshold_uncertainty_score":0.9987922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4760258199259716,"score_gpt":0.6348989246194889,"score_spread":0.1588731046935173,"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."}}