{"id":"W2808484680","doi":"","title":"Сравнительный анализ методик исчисления ВВП на региональном уровне","year":2018,"lang":"ru","type":"article","venue":"Вопросы статистики","topic":"Economic and Technological Developments in Russia","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subsidy; Gross Regional Product; Estimation; Attractiveness; Economics; Regional policy; Investment (military); Government (linguistics); Valuation (finance); Regional science; Political science; Economy; Geography; Finance; Politics; Market economy","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","research_integrity","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.001802854,0.0008758372,0.001067419,0.0003505223,0.002207927,0.0005243571,0.002599872,0.00151382,0.0266811],"category_scores_gemma":[0.0009068873,0.0008320346,0.0004327158,0.001431158,0.004023233,0.0006738529,0.0009857811,0.001047021,0.02650533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006941673,"about_ca_system_score_gemma":0.0007977852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001090345,"about_ca_topic_score_gemma":0.001147754,"domain_scores_codex":[0.9933079,0.0003530936,0.001403259,0.001743096,0.0009353744,0.00225722],"domain_scores_gemma":[0.9966561,0.0004152595,0.0006624835,0.001222938,0.0003002858,0.0007429829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001681718,0.0009640401,0.01798359,0.0001169359,0.000665899,0.00022845,0.01396531,0.000004853282,0.0004344649,0.4885966,0.2557371,0.2211346],"study_design_scores_gemma":[0.001050827,0.0004278003,0.0168013,0.0002098303,0.0001081848,0.00001907289,0.00387716,0.0001360715,0.001519823,0.04911387,0.9252616,0.001474515],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1800057,0.001217138,0.0006422346,0.01211304,0.00741867,0.001080097,0.0001214654,0.001180267,0.7962214],"genre_scores_gemma":[0.9151212,0.001334007,0.003269719,0.002526195,0.002995652,0.00007466678,0.00002880277,0.000100124,0.07454967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7351155,"threshold_uncertainty_score":0.9997824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03465865992300685,"score_gpt":0.3095789349151965,"score_spread":0.2749202749921896,"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."}}