Salvage logging after wildfire in the boreal forest: Is it becoming a hot issue for wildlife?
Bibliographic record
Abstract
In recent years, the increase in wood demand, the reduction in the availability of timber resources and the northern expansion of timber harvesting, along with the general perception that wildfires create ecological disasters, have favoured an increase in salvage logging in burned boreal forests. Concurrently, pioneer studies have shown that these post-fire forests may represent important habitats for several wildlife species and that intensive salvage logging, by removing standing snags, has several impacts on wildlife. However, the effects of salvage logging on biodiversity have yet to be considered in post-fire management plans. We examine the issue of salvage logging for wildlife in the boreal forest, with particular reference to Québec as an example. We describe our current state of knowledge on the use of burned forests by some wildlife and on the impacts of salvage logging on these habitats. We conclude that snag retention at multiple spatial and temporal scales in recent burns, which will be salvage-logged, is a prescription that must be implemented to meet the principles of sustainable forest management and the maintenance of biodiversity in the boreal forest. Key words: boreal forest, post-fire forests, salvage logging, snags, wildlife, birds, cavity-nesting birds, woodpeckers, mammals, invertebrates, xylophagous insects, biodiversity
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".