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Record W4320004814 · doi:10.1002/ecs2.4378

Sex‐dependent habitat selection modulates risk management by meadow voles

2023· article· en· W4320004814 on OpenAlexafffund
Douglas W. Morris

Bibliographic record

VenueEcosphere · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsLakehead University
FundersCanada Foundation for InnovationLakehead UniversityOntario Innovation Trust
KeywordsForagingTerritorialityPredationHabitatEcologyBiologyPopulationSelection (genetic algorithm)GeographyDemography

Abstract

fetched live from OpenAlex

Abstract Foraging involves a trade‐off between food and safety. Most research into the trade‐off invokes safety from predation. But danger and its associated risk arise from multiple causes that cannot be assessed solely with reference to predators. A more complete assessment of risk management requires experimental designs that attempt to modify and measure risks, regardless of the source of danger. I aimed to do so by adding shelter (mulched straw) and time‐varying supplemental food (rabbit chow), while measuring foraging behavior and habitat use by a seminatural population of meadow voles. Voles foraged more intensely under safety, recognized least risk when given access to both food and shelter, but altered their risk management through time: management included a novel form of sex‐dependent habitat selection in which male–male pairs occupied risky areas without shelter while female–female pairs occupied habitats sheltered by straw. The pattern is consistent with a sex‐dependent evolutionary game in which female territoriality and tolerance of other females limit conflict with, and space use by, males. Voles' array of interacting strategies demonstrates that ecologists must be wary of ascribing risk only to predation, and particularly so if experiments are blind to other dangers and processes that alter foraging behavior and habitat selection.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.996

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)0.0050.012

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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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