{"id":"W6903276815","doi":"10.1051/e3sconf/202347001013/pdf","title":"Greenhouse gas balance of Russia: the specifics of the federal districts","year":2023,"lang":"en","type":"article","venue":"Springer Link (Chiba Institute of Technology)","topic":"Advanced Power Generation Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Siberian Branch, Russian Academy of Sciences; Ministry of Science and Higher Education of the Russian Federation; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Greenhouse gas; Fossil fuel; Coal; Electricity generation; Energy balance; Context (archaeology); Greenhouse effect; Energy policy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001628375,0.0001303857,0.0002127469,0.001277883,0.0004243628,0.001154362,0.0001662403,0.000170921,0.001360497],"category_scores_gemma":[0.0003294123,0.0001060298,0.0003365603,0.0009346548,0.0002597638,0.000430623,0.0004696737,0.0001968981,0.000200711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001100298,"about_ca_system_score_gemma":0.0005271633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01401862,"about_ca_topic_score_gemma":0.01031167,"domain_scores_codex":[0.9998277,0.00003654252,0.00001014223,0.00003943005,0.00003427716,0.00005179197],"domain_scores_gemma":[0.9999121,0.00001290395,0.00002526708,0.0000107299,0.00002825658,0.00001067659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005679289,0.00007700958,0.5730776,0.000479661,0.0005104818,0.0006819893,0.002033532,0.08194377,0.01802858,0.1790672,0.004376448,0.1391559],"study_design_scores_gemma":[0.00001282234,0.00006592836,0.9273995,0.00008511666,0.0001418099,0.0004559268,0.001075735,0.01007706,0.004132264,0.01169797,0.0448246,0.00003128608],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9048128,0.003084312,0.005543284,0.0002569636,0.00005606527,0.00003033623,0.004514653,0.0001398453,0.08156174],"genre_scores_gemma":[0.9975533,0.0003043324,0.0002894568,0.000006950139,0.00001012545,0.000006178983,0.0005815677,0.0000105915,0.001237491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01401862,"threshold_uncertainty_score":0.02787405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335867214253455,"score_gpt":0.2194835463422099,"score_spread":0.2061248741996753,"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."}}