Impact of postfire management on forest regeneration in a managed hemiboreal forest, Estonia
Bibliographic record
Abstract
Fire is a significant agent for the development of boreal and hemiboreal forests, altering soil and light conditions, affecting seedbanks, and removing seed trees. Burned areas should be managed with care, as inappropriate techniques prolong the regeneration period and reduce the diversity and resilience of stands to disturbances. To study the effects of fire and postfire management on the successional changes in regeneration abundance, species composition and tree height sample plots were established in sandy pine forests in northwestern Estonia on areas burned 2 or 22 years ago. Five types of sample plots were established: (i) areas without fire damage, (ii) burned uncleared areas, (iii) burned forest areas cleared after forest fire, (iv) burned uncleared areas with live trees, and (v) burned uncleared areas with dead trees. Three main tree species common to hemiboreal forests were analyzed: Betula spp., Pinus sylvestris L., and Populus tremula L. Results showed that clearing burned areas after wildfire significantly reduced the abundance of regeneration compared with burned uncleared areas but favored height growth of P. sylvestris in later development. To regenerate and maintain mixed stands after wildfire, retaining some residual trees can facilitate regeneration compared with complete clearing, although a dense stand with live trees or a large amount of deadwood can hinder regeneration.
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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 | 0.000 | 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".