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RELATIONSHIP BETWEEN BODY CONDITION AND VULNERABILITY TO PREDATION IN RED SQUIRRELS AND SNOWSHOE HARES

2002· article· en· W2177598434 on OpenAlexaff
Aaron J. Wirsing, Todd D. Steury, Dennis L. Murray

Bibliographic record

VenueJournal of Mammalogy · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsSimon Fraser University
FundersIdaho Department of Fish and GameU.S. Forest ServiceMassachusetts Department of Fish and Game
KeywordsPredationSnowshoe harePredatorBiologyEcologyZoology

Abstract

fetched live from OpenAlex

We examined the relationship between physical condition and vulnerability to predation in 2 species of mammals having different life history traits. We predicted that predators would be more likely to kill substandard individuals disproportionately in red squirrels (Tamiasciurus hudsonicus) than in snowshoe hares (Lepus americanus), given that sciurids apparently are less susceptible to predation. We also predicted that differences would exist between patterns of substandard-prey selection among groups of predators of red squirrels but not among predators of snowshoe hares because of differential predator efficiencies in capturing elusive prey. Through radio tracking, we found that substandard squirrels (n = 113) were disproportionately vulnerable to predation, whereas hares (n = 125) were killed irrespective of condition. Although the relationship between condition and vulnerability to predation, relative to predator groups, did not differ significantly for either species of prey, the difference was qualitatively greater in squirrels than in hares. These results support the notion that predators are more likely to kill substandard individuals disproportionately when targeting prey species that are difficult to capture and, moreover, that the tendency for particular species of predators to take substandard prey may be a reflection of the predator's hunting strategy and efficiency.

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)0.000
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 score0.002
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.045
GPT teacher head0.298
Teacher spread0.253 · 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

Citations60
Published2002
Admission routes1
Has abstractyes

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