Effects of Burn Severity and Environmental Conditions on Post-Fire Regeneration in Siberian Larch Forest
Bibliographic record
Abstract
Post-fire forest regeneration is strongly influenced by abiotic and biotic heterogeneity in the pre- and post-fire environments, including fire regimes, species characteristics, landforms, hydrology, regional climate, and soil properties. Assessing these drivers is key to understanding the long-term effects of fire disturbances on forest succession. We evaluated multiple factors influencing patterns of variability in a post-fire boreal Larch (Larix sibirica) forest in Siberia. A time-series of remote sensing images was analyzed to estimate post-fire recovery as a response variable across the burned area in 1996. Our results suggested that burn severity and water content were primary controllers of both Larch forest recruitment and green vegetation cover as defined by the forest recovery index (FRI) and the fractional vegetation cover (FVC), respectively. We found a high rate of Larch forest recruitment in sites of moderate burn severity, while a more severe burn was the preferable condition for quick occupation by vegetation that included early seral communities of shrubs, grasses, conifers and broadleaf trees. Sites close to water and that received higher solar energy during the summer months showed a higher rate of both recovery types, defined by the FRI and FVC, dependent on burn severity. In addition to these factors, topographic variables and pre-fire condition were important predictors of post-fire forest patterns. These results have direct implications for the post-fire forest management in the Siberian boreal Larch region.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".