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Friends because of foes: the interplay between space use and sociality in mediating predation risk

2021· preprint· en· W3127924266 on OpenAlexaff
Christina M. Prokopenko, Edward Ellington, Alec L. Robitaille, Jaclyn A. Aubin, Juliana Balluffi‐Fry, Michel P. Laforge, Quinn M. R. Webber, Sana Zabihi‐Seissan, Eric Vander Wal

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSocialityPredationEcologyPredatorTraitBiologyHabitatVulnerability (computing)Foraging

Abstract

fetched live from OpenAlex

From the perspective of prey, movement synchrony can represent either a potent anti-predator strategy or a dangerous liability. Prey must balance the costs and benefits of using conspecifics to mediate risk and the emergent patterns of risk-driven sociality depends on the spatial variation and trait composition of the system. Our literature review outlined the prevailing, but not universal, trend of animals using sociality as an antipredator strategy. Empirically, we then used movement synchrony as a measure of social antipredator response of two ungulates to spatial variation in predator and prey habitat domains. We demonstrated that these responses vary based on prey vulnerability and predator hunting modes. Prey favored asynchrony when calves were present and within habitat domains of ambush predators but not pursuit predators. By unifying community ecology concepts such as habitat domains with movement ecology we provided a comprehensive evaluation of factors mediating prey social response to predation risk.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.316
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

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