Habitat use patterns in relation to escape terrain: are alpine ungulate females trading off better foraging sites for safety?
Bibliographic record
Abstract
Predation risk often forces prey to trade off good foraging sites for safety, and compromises are expected to be greater in females with vulnerable offspring than in barren females. To determine whether adult females of large herbivores traded off forage for safety, we assessed habitat use and estimated vegetation abundance and quality in relation to distance to escape terrain in marked mountain goats ( Oreamnos americanus de Blainville, 1816). We found that all females spent more time foraging near escape terrain than away from them. Females with young foraged on average 20 m closer to escape terrain than barren females in June, a time when offspring were particularly vulnerable to predation. Plant biomass did not vary with distance to escape terrain in June, but was lower closer than away from escape terrain during all other months. The abundance of forbs and shrubs increased with distance to escape terrain, but their quality did not vary. For grasses and sedges, plant digestible content decreased closer to escape terrain, but interestingly proteins increased. Our results suggest that females traded off forage abundance, and to a lesser extent forage quality, for safety. Compared with barren females, females with offspring may face a trade-off in plant digestible content by foraging in safer areas than barren females.
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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) | 0.001 | 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".