Effects of selection cuts on winter habitat use of snowshoe hare (<i>Lepus americanus</i>) in northern temperate forests
Bibliographic record
Abstract
Selection cutting is used in northern temperate forests where regeneration dynamics are driven by gap formation. By creating openings in the canopy, selection cutting modifies shrub cover, an important criterion in winter habitat selection by snowshoe hare (Lepus americanus Erxleben, 1777), a key species in North American forests. The objective of this study was to determine the effects of selection cuts on snowshoe hare habitat and to evaluate the restoration of habitat quality over time. Occurrence indices for snowshoe hare (fecal pellets and tracks) were modelled according to habitat quality parameters for 22 hardwood stands that were subjected to selection cutting between 1993 and 2007 and 30 untreated stands (15 hardwood and 15 mixedwood) in Abitibi-Témiscamingue, Quebec. Model selection based on the Akaike second-order information criterion (AICc) identified lateral cover as the only habitat structure parameter having a positive effect on snowshoe hare abundance in the study sites. Indicators of snowshoe hare presence were highest in untreated mixedwood stands but more abundant in selection cuts than in untreated hardwood stands. The use of selection cuts by snowshoe hare increased with time since logging was performed. We conclude that selection cutting exerted a positive effect on the use of hardwood stands by snowshoe hare.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".