Analysis of Carbon Sequestration Potential of Forests of the Asian Russia
Bibliographic record
Abstract
We estimated the amount of carbon dioxide (CO2) sequestration and release by managed forests in Siberia and the Russian Far East. The data from "National report of the Russian Federation on the inventory of human-induced emissions by sources and removals by sinks of greenhouse gases not controlled under the Montreal Protocol for 1990-2010" served as input data. We calculated the amounts of CO2 taken up and released. The net CO2 flux is a difference between the CO2 sequestrated and CO2 released. The sequestration potential of forests depends on the climatic conditions of the area and the species of woody plants growing there. Many forests die every year, and the CO2 release by forests is caused by clear-cuttings and natural disasters. The highest sequestration rate of forests was observed in Omsk and Irkutsk regions, the lowest in the Chukotka autonomous district and Magadan region. The largest amounts of CO2 were sequestrated in the Republic of Sakha (Yakutia) and Krasnoyarsk territory. The highest release rates were observed in the Chukotka autonomous district and the Khabarovsk territory, the lowest – in the Novosibirsk region, Kemerovo region, and Kamchatka territory. We conclude that nearly half of the total CO2 sequestration by managed forests in Russia was contributed by its Asian regions, with 27.5% by the Siberian Federal District and 20.9% by the Russian Far East
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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) | 0.000 | 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 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".