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Record W2143295749 · doi:10.2980/21-1-3689

Male and female voles do not differ in their assessments of predation risk

2014· article· en· W2143295749 on OpenAlexaffvenue
William D. Halliday, Douglas W. Morris, Jordan A. Devito, Denon Start

Bibliographic record

VenueEcoscience · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsLakehead University
Fundersnot available
KeywordsForagingBiologyReproductive successPredationEcologyForageReproductionDemographyZoologyPopulation

Abstract

fetched live from OpenAlex

A forager's willingness to trade off safety for food varies with its energetic state. Animals in a low energetic state should accept higher risk than animals with larger energy reserves. In mammals, energy expenditure by females on gestation and lactation may exceed the relatively low cost of sperm production by males. It follows, if reproductive costs are indeed higher for females than for males, that reproductive females may be more likely than males to trade safety for food. Thus, we evaluated the use of safe versus risky foraging patches by male and female meadow voles using putatively safe and risky habitats. We also used behavioural trials to assess whether sexual differences in personality could account for any differences in patch use. Voles preferred to forage in safe patches over risky ones. There was no difference between male and female voles, or between reproductive and non-reproductive individuals, in their respective use of safe versus risky foraging patches. Personality also had no effect on patch choice. The results are consistent with recent studies on other species that have failed to find differences in reproductive costs between the sexes. Experiments on foraging behaviour might thus provide simple and repeatable tests for sexual differences and similarities in reproductive costs.

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 score0.378
Threshold uncertainty score0.066

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 head0.026
GPT teacher head0.254
Teacher spread0.228 · 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

Citations5
Published2014
Admission routes2
Has abstractyes

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