Factors affecting browsing by moose (<i>Alces alces</i>L. ) on European aspen (<i>Populus tremula</i>L.) in a managed boreal landscape
Bibliographic record
Abstract
There is considerable circumpolar concern regarding the regeneration of several tree species in the temperate and boreal landscape due to heavy browsing. We analyzed the risk of browsing on aspen (Populus tremula L.) at two different scales in a managed boreal forest in northern Sweden with one dominating browser in the system, the moose (Alces alces L.). At the stand level, we found that a high density of aspen ramets in connection to or surrounded by young forest (predominantly Scots pine Pinus sylvestris L.) attracted moose relatively more than aspen stands in mature forest and interior forest, respectively. If a stand was being used, a single aspen ramet faced the best chance of escaping browsing in a stand with a high density of aspen ramets, located far from arable land. This utilization pattern by the herbivores suggests that older forest may function as a temporal refuge for aspen regeneration in the managed boreal landscape, but this situation may change as remaining old forest stands eventually turn into young forest. Although cutting will favour aspen regeneration, our study highlights an apparent paradox, as the emerging aspen ramets will face a high browsing risk from attracted herbivores.
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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 | 0.001 |
| 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 | 0.001 | 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".