Response of beaver, moose, and snowshoe hare to clear-cutting in a Quebec boreal forest: a reassessment 10 years after cut
Bibliographic record
Abstract
We studied the response of beaver (Castor canadensis Kuhl), moose (Alces alces L.), and snowshoe hare (Lepus americanus Erxl.) to clear-cutting in three blocks that had been logged 10 years ago. In a previous study, these species had been surveyed in the same blocks 2 years before and 2 years after logging. We also surveyed an uncut block of the initial experimental design that was logged more recently. Over the 10-year period, the shrub layer and available browse have improved markedly in clear-cut areas. As compared with logged coniferous stands, logged mixed stands had higher lateral cover (62% vs. ≈55%) and taller regeneration (>4 m vs. <3 m). Beaver density did not change over the period because its feeding habitat remained unchanged in the riparian forest strips. Moose densities increased 54%87% in two harvested blocks as a result of both logging and stricter hunting regulations (selective hunting). Based on the rate of increase observed in a control block, we estimate that a 25% density increase in the mixed forest block can be related to logging, while selective hunting can solely be responsible for the change in the coniferous forest block. Snowshoe hare have started to reoccupy logged coniferous stands, but their relative density still remained less than half that of uncut stands. We conclude that, after 10 years, logged mixed stands already offer good habitat conditions for moose and snowshoe hare. Conversely, in logged black spruce (Picea mariana (Mill.) BSP) stands, habitat conditions still remain poor for snowshoe hare because of a lack of cover.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".