Chapter 7 Ecology of Snowshoe Hares in Southern Boreal and Montane
Bibliographic record
Abstract
Abstract—Snowshoe hares occur in many of the montane and sub-boreal forests of the continental United States, as well as throughout the boreal forests of Canada and Alaska. Population dynamics in their southern range were previously thought to be noncyclic, in contrast to the strong 10-year fluctuation that typifies boreal populations of snowshoe hares. Time series data and studies of hare demography indicate that northern and southern populations of hares may instead have similar population dynamics. Hares in southern areas appear to experience two- to 25-fold fluctuations in numbers with peaks eight to 11 years apart. Peak and low densities may be lower in southern areas than in northern ones; in the south, peak densities are commonly one to two hares/ha, whereas northern hare populations commonly have peak densities up to four to six hares/ha. Demographically, survival estimates (30-day) range from approximately 0.65-0.95 in Wisconsin, with lowest survival occurring as populations decline; these values parallel those of cyclic hares in Yukon. Annual reproductive output may vary regionally, but interpretation of this pattern is hindered by noncomparable methodologies. 163 Chapter 7—Hodges The southern range of snowshoe hares is roughly delineated by the range of suitable forested habitats. Along the eastern seaboard, hares use spruce/fir and deciduous forests as far south as Tennessee and the Virginias. Around the Great Lakes, hares occur throughout the sub-boreal coniferous forests. In the Rockies and westward, hares mainly use the coniferous forests that extend along the mountains down into New Mexico and California. Throughout their range, hares are predominantly associated with forests that have a well-developed understory that provides protection from predation and supplies them with food. Such habitat structure is common in early seral stages but may also occur in coniferous forests with mature but relatively open overstories or in eastern deciduous forests.
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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 | 0.000 | 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) | 0.004 | 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".