Greenhouse gas balance of Russia: the specifics of the federal districts
Bibliographic record
Abstract
\nThe paper is devoted to the analysis of the greenhouse gas balance developed by the authors for the main participating sectors in the context of federal districts based on actual data for 2021. On the one hand, the energy industry is involved, which is the sector of the economy that occupies a leading position in terms of greenhouse gas emissions – up to 80% of total emissions. Greenhouse gas emissions from the main sectors of the fuel and energy complex were estimated: power generation from fossil fuels and production of fuel and energy resources. On the other hand, the volumes of CO2 absorbed by managed forests of the forest fund, which are the main sink of CO2, were calculated, taking into account losses caused by logging, fires and other causes. The calculated estimates of greenhouse gas emissions showed that the main inflow in all subjects of the Russian Federation comes from energy generation: the largest emission is in the Ural, Central and Siberian federal districts. In terms of greenhouse gas emissions, the Siberian Federal District stands out in coal production, and the Urals Federal District in hydrocarbon production. The largest contribution to the absorption of carbon dioxide is made by the Siberian and Far Eastern Federal Districts. The contribution of these districts to the total figure for Russia is almost half. The only federal district with a negative net balance of greenhouse gases, as determined by the study, is the Far East.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".