The response of mammals to forest fire and timber harvest in the North American boreal forest
Bibliographic record
Abstract
ABSTRACT 1. This paper reviews and compares the effects of forest fire and timber harvest on mammalian abundance and diversity, throughout successional time in the boreal forest of North America. 2. Temporal trends in mammal abundance and diversity are generally similar for both harvested and burned stands, with some differences occurring in the initiation stage (0–10 years post disturbance). 3. Small mammals and ungulates are most abundant immediately post disturbance, and decrease as stands age. Lynxes and hares utilize mid‐successional stands, but are rare in young and old stands. Bats, arboreal sciurids and mustelids increase in abundance with stand age, and are most abundant in old growth. 4. Substantial gaps in the data exist for carnivores; the response of these species to fire and harvest requires research, as predator–prey interactions can affect mammal community structure in both early and late successional stages. 5. The lack of explicit treatment of in‐stand forest structure post disturbance, in the reviewed literature made comparisons difficult. Where forest structure was considered, the presence of downed woody material, live residual trees and standing dead wood were shown to facilitate convergence of mammal communities to a pre‐disturbance state for both disturbance types. 6. Mammalian assemblages differed considerably between successional stages, emphasizing the importance of maintaining stands of each successional stage on the landscape when implementing forest management strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".