Response of moose to forest harvest and management: a literature review
Bibliographic record
Abstract
Moose are an iconic symbol of northern forests. In many jurisdictions, the management of moose has focused on regulating harvest with less emphasis on understanding moose–habitat relationships. We reviewed the literature and summarised the effects of forest harvest and management on the ecology of moose. Greater than 50 years of scientific studies document both positive and negative effects of forest harvest and associated activities such as silviculture and road building. Moose require spatially adjacent patches of younger plant communities for forage and older forests for thermal and security cover. Extensive and rapid forest harvest can result in the prevalence of young forest with a corresponding reduction in the fitness of moose populations. A warming climate likely will exacerbate the negative effects associated with the broad-scale removal of forest cover. Resource roads can create edge habitat that may serve as forage, but those features result in increased hunting and collisions with vehicles and facilitate the movement of predators. Post-harvest silviculture, including the application of herbicides, can create stand conditions that provide very little or low-quality forage. The ecological and societal benefits of moose are dependent on forest management that provides a mix of old and young forest, employs silviculture that retains adequate cover and forage plants, and minimises the development of roads.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".