Influence of logging on the effects of wildfire in Siberia
Bibliographic record
Abstract
The Russian boreal zone supports a huge terrestrial carbon pool. Moreover, it is a tremendous reservoir of wood products concentrated mainly in Siberia. The main natural disturbance in these forests is wildfire, which modifies the carbon budget and has potentially important climate feedbacks. In addition, both legal and illegal logging increase landscape complexity and affect burning conditions and fuel consumption. We investigated 100 individual sites with different histories of logging and fire on a total of 23 study areas in three different regions of Siberia to evaluate the impacts of fire and logging on fuel loads, carbon emissions, and tree regeneration in pine and larch forests. We found large variations of fire and logging effects among regions depending on growing conditions and type of logging activity. Logged areas in the Angara region had the highest surface and ground fuel loads (up to 135 t ha −1 ), mainly due to logging debris. This resulted in high carbon emissions where fires occurred on logged sites (up to 41 tC ha −1 ). The Shushenskoe/Minusinsk and Zabaikal regions are characterized by better slash removal and a smaller amount of carbon emitted to the atmosphere during fires. Illegal logging, which is widespread in the Zabaikal region, resulted in an increase in fire hazard and higher carbon emissions than legal logging. The highest fuel loads (on average 108 t ha −1 ) and carbon emissions (18–28 tC ha −1 ) in the Zabaikal region are on repeatedly burned unlogged sites where trees fell on the ground following the first fire event. Partial logging in the Shushenskoe/Minusinsk region has insufficient impact on stand density, tree mortality, and other forest conditions to substantially increase fire hazard or affect carbon stocks. Repeated fires on logged sites resulted in insufficient tree regeneration and transformation of forest to grasslands. We conclude that negative impacts of fire and logging on air quality, the carbon cycle, and ecosystem sustainability could be decreased by better slash removal in the Angara region, removal of trees killed by fire in the Zabaikal region, and tree planting after fires in drier conditions where natural regeneration is hampered by soil overheating and grass proliferation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".