Attributes of forest strips used by snowshoe hare in winter within clear-cut boreal landscapes
Bibliographic record
Abstract
The snowshoe hare (Lepus americanus Erxleben) is an important prey for many predators in the boreal forest. In this biome, clear-cut landscapes are generally large and consist of aggregated cutting blocks separated by narrow forest strips (typically 60100 m wide). To identify attributes of forest strips that are important for snowshoe hares, we measured the use of strips using track counts over two winters in six clear-cut landscapes (23256 km2) in south-central Quebec. Surveys were conducted in 20 riparian strips (RS), 20 upland strips (US), and 15 control sites (CO) at the periphery of clear-cut landscapes. Overall, 392 signs of hare presence were recorded along 50 km of transects. Snowshoe hares were present in one-third of the strips surveyed and were five times less abundant in US and RS than in CO. The species avoided strip edges. Hares were more common in the wider strips (>100 m), in the strips adjacent to residual forest patches (≥25 ha), or in those having a denser shrub canopy, which is often associated with a greater basal area in balsam fir (Abies balsamea (L.) Mill.). To maintain snowshoe hare at moderate densities in large clear-cut landscapes, we suggest leaving uncut forest strips >100 m wide in areas having a good shrub cover with presence of balsam fir.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".