{"id":"W6888902219","doi":"10.24411/2500-1000-2020-10327","title":"СРАВНИТЕЛЬНЫЙ АНАЛИЗ НАЛОГОВЫХ СИСТЕМ РОССИИ, КАНАДЫ, ШВЕЙЦАРИИ И ЮЖНОЙ КОРЕИ","year":2020,"lang":"ru","type":"article","venue":"CyberLeninK (CyberLeninka)","topic":"Education, Law, and Society","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Tax reform; Economic analysis; Tax revenue; Tax credit","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":[0.003129426,0.002167014,0.002530421,0.0003179986,0.004382597,0.001604245,0.00407533,0.002025704,0.01038587],"category_scores_gemma":[0.002410214,0.002421114,0.00216532,0.004150281,0.003343248,0.002138582,0.0008844093,0.002298862,0.005331658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141321,"about_ca_system_score_gemma":0.003509042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005445576,"about_ca_topic_score_gemma":0.003640434,"domain_scores_codex":[0.9838648,0.001791846,0.002793559,0.003692825,0.00365962,0.004197283],"domain_scores_gemma":[0.9892951,0.001640821,0.001599281,0.002268052,0.001332326,0.003864405],"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":[0.0002072421,0.001163358,0.021644,0.0004915269,0.001060575,0.0001271792,0.3668494,0.00007077712,0.001150946,0.09377493,0.4932657,0.02019435],"study_design_scores_gemma":[0.002931648,0.0005537885,0.01108284,0.0004069364,0.0008768469,0.00002484337,0.09519593,0.0004063074,0.000818933,0.004221423,0.8799317,0.003548735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4599795,0.01462257,0.001463396,0.1409785,0.017278,0.004935,0.0008963879,0.002986599,0.3568601],"genre_scores_gemma":[0.8990464,0.006298486,0.003917704,0.01300867,0.01809746,0.0003002048,0.0003893736,0.0005600409,0.05838167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4390669,"threshold_uncertainty_score":0.9994322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04566042871022696,"score_gpt":0.3123282287673234,"score_spread":0.2666678000570964,"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."}}