Short-term changes in spatial distribution pattern of an herbivore in response to accumulating snow
Bibliographic record
Abstract
Deep snow can reduce accessibility to vegetation and cover by herbivores by blanketing understory cover, yet simultaneously increase access to foliage at higher levels. Thus, snow depth fluctuation should lead to spatiotemporal variation in herbivore habitat use. We measured shifts in habitat use by snowshoe hare (Lepus americanus Erxleben, 1777) as a function of snow depth in an eastern Canadian boreal forest where snow depth often exceeds 1 m. We hypothesized that as snow accumulates, snowshoe hares shift from locations with dense vegetation just above ground to locations with dense vegetation higher above ground. We surveyed 58 km of transects over three winters and found 1954 hare tracks. We analyzed track counts as a response to a density index of low vegetation (0–1.5 m above ground), high vegetation (2–4 m above ground), predator tracks, and snow depth. We found more hare tracks in sites with dense high vegetation when snow was deeper, and more hare tracks in sites with dense low vegetation when snow was shallower. Predator track presence did not influence responses to snow depth. Snow depth dynamics can drive hare distribution, and in turn, introduce uncertainty in spatial distribution models for the species and possibly its interactions with predators.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".