Effects of precommercial thinning on snowshoe hare habitat use during winter in low-elevation montane forests
Bibliographic record
Abstract
We assessed snowshoe hare (Lepus americanus L.) habitat use during winter on two precommercial thinning treatments in sapling stands in northwestern Montana, USA. One treatment type retained 0.2-ha patches of unthinned saplings, representing 8% of the total stand area, and the second retained 0.8-ha patches of unthinned saplings, representing 35% of the stand area. Snowshoe hare habitat use was also estimated within a nearby control sapling stand and mature conifer stands. We used snow tracking and fecal pellet counts to estimate use before and after thinning treatments were applied. Although we did not find a conclusive trend in hare use of sapling stands after thinning, use within the control stand and adjacent mature stands suggested there was considerable movement of hares to nearby untreated stands after thinning. Hares used retention patches regardless of size, even though large retention patches were four times larger than small retention patches. Because hares demonstrated an affinity for dense patches of residual forest, any retention of untreated saplings may be beneficial for hares during winter when applying precommercial thinning treatments in areas where stand sizes are relatively small (1014 ha), and the resulting thinned matrix is less harsh than in larger thinned stands. Use within thinned portions of the stand and unthinned remnant blocks suggests that over the winter hares may also benefit from a connectivity of dense cover.
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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 | 0.000 | 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 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".