The effects of timber harvest, forest fire, and herbivores on regeneration of deciduous trees in boreal pine-dominated forests
Bibliographic record
Abstract
Forest management, fire, and herbivores are the major factors affecting regeneration of deciduous trees in boreal forests. In a large-scale experiment, we manipulated the use of prescribed burning, the level of green-tree retention and the presence of moose ( Alces alces L.) and hare ( Lepus timidus L. and Lepus europaeus Pallas) to study their effects on early regeneration of three native pioneer tree species, i.e., rowan ( Sorbus aucuparia L.), aspen ( Populus tremula L.), and silver birch ( Betula pendula Roth). Green-tree retention enhanced survival of all tested tree species. Prescribed burning enhanced the survival rate of birch and rowan, but aspen survival was only enhanced by burning on clearcuts and areas with 50 m3/ha of retention trees. Excluding moose enhanced rowan growth and birch survival. Aspen growth and survival was enhanced when both moose and hare were excluded. Seedlings were most frequently browsed on clearcuts, and most seedling mortality was caused by voles or hare. At low densities, the effect of moose on pioneer trees may be smaller than that of other herbivores or the fire–management regime. Considering the large number of species depending on pioneer trees, the results support the use of tree retention and fire as useful management alternatives not only to promote biodiversity but also to enhance regeneration of deciduous trees and reduce herbivore damage.
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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) | 0.000 | 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".