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Record W2969268917 · doi:10.1139/facets-2018-0048

Contingent strategies of risk management by snowshoe hares

2019· article· en· W2969268917 on OpenAlexaffvenue
Douglas W. Morris

Bibliographic record

VenueFACETS · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsLakehead University
Fundersnot available
KeywordsPredationVigilance (psychology)HabitatEcologySnowshoe hareBiologyPredatorForagingGeography

Abstract

fetched live from OpenAlex

Prey individuals employ several adaptive behaviours to reduce predation risk. We need to learn how those behaviours interact in an overall strategy of risk management, how strategies vary with changing conditions, and whether some behaviours might compensate for others. I addressed these issues with manipulative experiments evaluating how snowshoe hares’ ( Lepus americanus) vigilance varies with their giving-up densities (GUDs) in artificial food patches. I tested whether the results, collected when there was no evidence of predation, were congruent with an earlier study under higher predation. When predator sign was common, vigilance depended directly on habitat. But when risk was low, habitat’s influence on vigilance was indirect. Hares were least vigilant during the new moon where the distance to escape habitat was far, but only in open risky habitat. Hares were more vigilant during the full moon, but only at stations far from escape habitat. Moon phase and additional cover had no effect on GUDs that were highest at open risky stations far from escape habitat. The results suggest that reduced risk allowed hares to allocate less time to vigilance, but they needed to forage for similar amounts of food during each moon phase to maintain their energetic state.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.666

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)0.0010.000

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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