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Record W4364355917 · doi:10.1515/mammalia-2023-0008

Digging deep: hoary marmots (<i>Marmota caligata</i>) use refuge burrows excavated by grizzly bears (<i>Ursus arctos</i>)

2023· article· en· W4364355917 on OpenAlexaffabout
Thomas S. Jung, Sarah M. Arnold, Alexandra Heathcote, Piia M. Kukka, Caitlin N. Willier, Alice M. McCulley, Shannon A. Stotyn, Kirsten Wilcox

Bibliographic record

VenueMammalia · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsGovernment of CanadaEnvironment and Climate Change CanadaParks CanadaYukon Department of EnvironmentUniversity of Alberta
Fundersnot available
KeywordsBurrowMarmotUrsusForagingGrizzly BearsDiggingGeographyEcologyBiologyArchaeologyPopulation

Abstract

fetched live from OpenAlex

Abstract Hoary marmots ( Marmota caligata ) dig burrows in alpine meadows rich in forage as ready refuge from potential predators. Refuge burrows enable hoary marmots to engage in risk-sensitive foraging when they are away from more secure resting burrows on talus slopes. Grizzly bears ( Ursus arctos ) commonly excavate refuge burrows while hunting marmots, substantially changing the physical characteristics of the burrow by removing earthen material. However, it is not known if marmots continue to use excavated burrows as refuge. We opportunistically inspected 22 burrows excavated by grizzly bears for use by marmots at two sites in northwestern Canada. We found marmot feces at 10 of 22 excavated burrows, indicating that marmots continued to use these burrows after they were excavated by bears. While marmots may dig several refuge burrows in alpine meadows, and bears may substantially modify them while hunting marmots, our observations indicate that some excavated burrows retain value for foraging marmots. However, the extent of their utility is unknown. We postulate several reasons why hoary marmots may use excavated burrows and suggest avenues for further research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.005

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 head0.015
GPT teacher head0.221
Teacher spread0.207 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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