{"id":"W116368669","doi":"","title":"Опыт анализа динамики больших временных рядов демографических параметров стран мира и России","year":2013,"lang":"ru","type":"article","venue":"Пространство и Время","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Life expectancy; Population; Per capita; Demography; Fertility; Developed country; China; Developing country; Total fertility rate; Infant mortality; Geography; Economics; Research methodology; Family planning; 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":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","insufficient_payload"],"category_scores_codex":[0.004006648,0.002174855,0.002359751,0.001214189,0.003202947,0.002218761,0.004351283,0.001484209,0.01897312],"category_scores_gemma":[0.0006961853,0.002263837,0.001775583,0.00416418,0.002972252,0.00278017,0.001390168,0.002086892,0.03002146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000753505,"about_ca_system_score_gemma":0.0008194714,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04470346,"about_ca_topic_score_gemma":0.009349088,"domain_scores_codex":[0.9822407,0.001860872,0.003038271,0.003383377,0.004444395,0.005032367],"domain_scores_gemma":[0.9910934,0.0006433483,0.001754002,0.00350704,0.001163364,0.001838822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002012218,0.003401601,0.2139975,0.001201682,0.00219146,0.0003907552,0.01953158,0.0002117752,0.0006884931,0.09590839,0.528327,0.1339484],"study_design_scores_gemma":[0.00283102,0.0005955274,0.2159296,0.0005517282,0.0008283058,0.00001706844,0.01391303,0.0009055915,0.0004113089,0.01787645,0.7420775,0.0040629],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.503824,0.006950388,0.0004653242,0.01352338,0.01629965,0.007803631,0.0003333875,0.001755331,0.4490449],"genre_scores_gemma":[0.9267499,0.004733844,0.001215645,0.003981298,0.00445035,0.0008942113,0.0001312317,0.0003796802,0.05746377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.422926,"threshold_uncertainty_score":0.9998121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01500778791934947,"score_gpt":0.2749334201839324,"score_spread":0.2599256322645829,"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."}}