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Record W2058799147 · doi:10.1139/z07-080

Habitat use patterns in relation to escape terrain: are alpine ungulate females trading off better foraging sites for safety?

2007· article· en· W2058799147 on OpenAlexaffvenue
Sandra Hamel, Steeve D. Côté

Bibliographic record

VenueCanadian Journal of Zoology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsForagingUngulatePredationBiologyForbForageHerbivoreHabitatEcologyAbundance (ecology)Biomass (ecology)OffspringGrasslandVegetation (pathology)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.232
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations109
Published2007
Admission routes2
Has abstractyes

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