Vegetation structure and composition determine snowshoe hare (<i>Lepus americanus</i>) activity at arctic tree line
Bibliographic record
Abstract
Snowshoe hares (Lepus americanus Erxleben, 1777) are keystone herbivores supporting many boreal-forest predators. Understanding habitat use of hares can help predict how hares and their predators will be affected by disturbances such as climate change, which will have a particularly strong effect at tree line. We examined hare activity at arctic tree line near Churchill, Manitoba, using fecal pellet transects established in August 2012. We counted all hare fecal pellets at two plots per transect and measured plant abundance and vegetation structure surrounding the plots, then used model selection to determine the combinations of habitat features or plant species that best explained hare activity or presence. Hare pellets occurred at a higher density where tall shrub cover was high. Pellet density also increased with increasing canopy cover, tree size, tree density, visibility, and small shrub cover, but was negatively correlated with medium-sized shrub and herb cover. Hares preferred areas with willow (genus Salix L.) and avoided areas with unpalatable black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.). With climate warming, tree line is expected to advance northward and the increased tall shrub and willow cover predicted with increasing temperatures should benefit hares by providing greater predator protection and food.
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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 | 0.000 | 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) | 0.001 | 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".