{"id":"W4414618350","doi":"10.48647/icca.2025.72.11.015","title":"Достижения и перспективы развития технологий искусственного интеллекта в КНДР","year":2025,"lang":"ru","type":"article","venue":"Современные проблемы Корейского полуострова","topic":"COVID-19, Geopolitics, Technology, Migration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"State (computer science); Applications of artificial intelligence; Key (lock); Work (physics); Current (fluid)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003052817,0.000756488,0.0004757382,0.002673423,0.003773613,0.01242688,0.001230189,0.002638207,0.05353775],"category_scores_gemma":[0.007882209,0.0007011791,0.0009199265,0.002555723,0.004985881,0.005942615,0.003519971,0.003656239,0.01943781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005978156,"about_ca_system_score_gemma":0.01048257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01558851,"about_ca_topic_score_gemma":0.01484823,"domain_scores_codex":[0.9954326,0.001080268,0.0002513932,0.0007512009,0.001929287,0.0005551875],"domain_scores_gemma":[0.9958614,0.001055778,0.0004352602,0.000621072,0.00151754,0.0005090493],"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":[0.0001151408,0.0000808341,0.004163676,0.0003813578,0.00004577841,0.0004949418,0.0057273,0.0009870282,0.003073133,0.8530191,0.03233825,0.09957338],"study_design_scores_gemma":[0.00003112697,0.00005480936,0.007136494,0.0003720204,0.00004395454,0.0006029165,0.004439277,0.0009819546,0.002890937,0.1441811,0.8391774,0.00008802952],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03228129,0.007550098,0.0458229,0.0163319,0.001536592,0.0001946593,0.001103822,0.0004973146,0.8946814],"genre_scores_gemma":[0.5586166,0.01361157,0.05748098,0.003070708,0.0009052592,0.0007143864,0.001358193,0.0007782559,0.363464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05353775,"threshold_uncertainty_score":0.1791016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03314224714012329,"score_gpt":0.3791109842231166,"score_spread":0.3459687370829932,"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."}}