Vulnerability of larch forests to forest fires along a latitudinal gradient in eastern Siberia
Bibliographic record
Abstract
The predicted increase in frequency and intensity of boreal forest fires is considered a significant source of carbon dioxide emissions and linked with the degradation of permafrost covering more than half of Russia. Here we analyzed the stand structure and growth of East Siberian larch forests in response to fire severity. We measured 23 sites in the southern part of eastern Siberia along a latitudinal transect with a length of more than 1500 km. Live tree volume differed significantly across geographical regions ( p < 0.05), decreasing from south to north (76–250 m 3 ·ha −1 ), with higher values in forests burnt with low severity. Similarly, volume of coarse woody debris decreased from south to north. The volume of dead standing trees, on the other hand, increased from south to north. The distribution of trees by diameter class in some areas showed clear evidence of fires, with small trees being absent to rare in forests burnt at high severity. The impact of severe fires on stand volume was negligible at the southern sites, potentially associated with rapid regeneration of birch. Birch is an important component of larch forests near the southern boundary of the permafrost, which may contribute to larch forests in the southern part of the study transect being less vulnerable to wildfires compared to northern larch forests.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".