Snowshoe Hares (<i>Lepus americanus</i>) in the Western United States: Movement in a Dynamic Managed Landscape
Bibliographic record
Abstract
Abstract Since the listing of the Canada lynx, Lynx canadensis, as threatened in the coterminous United States (U.S. Fish and Wildlife Service 2000), the effect of landscape-scale forest management on populations of snowshoe hares, Lepus americanus, has been a concern. Snowshoe hares are the central prey species of lynx (Aubry et al. 2000). Because snowshoe hare densities vary widely across different types of forested vegetation structure (Adams 1959, Dolbeer and Clark 1975, Wolff 1980, Wolfe et al. 1982), changes in forest vegetation structure through natural disturbance, succession, and silviculture can have dramatic impacts on populations. Snowshoe hare populations cycle with a regular 9-to 11-year period in the northern portion of the species range (Keith 1990), but evidence of a cycle to the south is mixed (Hodges 2000). Vegetative succession complicates population models (Johnson 2000). In western Montana, we have found highest snowshoe hare densities in regenerating forest stands with high sapling density and in uncut, late-seral-stage forest stands also with abundant saplings. This high-quality snow-shoe hare habitat in Montana can be thought of as having a bimodal distribution relative to forest stand age, with only young and much older stands providing the “closed” understories with abundant cover and browse (Buskirk et al. 2000). The layer of abundant shrubs and saplings that regenerates following clearcuts or large fires later disappears as the lower limbs die on growing trees, but this layer may reappear when large trees die, creating canopy gaps (Oliver and Larson 1986). The potentially long period when the stand understory becomes and remains comparatively “open” is a time of lower habitat quality. Anthropogenic canopy gaps in partially harvested stands can also stimulate growth of a dense understory layer under an established canopy (DeBell et al. 1997, Tappeiner et al. 1997).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".