Greenhouse gas balance of Russia: the specifics of the federal districts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it