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CACHING OF RICHARDSON'S GROUND SQUIRRELS BY NORTH AMERICAN BADGERS

2000· article· en· W6962523012 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHoarding (animal behavior)BadgerCacheGround squirrelHibernation (computing)Predation

Abstract

fetched live from OpenAlex

Abstract In 5 autumns and 1 summer during a 13-year study of Richardson's ground squirrels (Spermophilus richardsonii) in southern Alberta, Canada, carcasses of 27 ground squirrels were scatter hoarded by badgers (Taxidea taxus). Cached ground squirrels, which rarely exhibited external signs of trauma, were hoarded singly either above ground (n = 16) or underground (n = 11) in a curled head-to-tail posture in a pocket of firm soil and then covered with loose soil. Except for 3 carcasses cached by a mother badger in June, remaining ground squirrels were cached between 7 September and 28 November, with most hoarding (n = 17) occurring from 16 October to 17 November. Of 24 ground squirrels cached in autumn, 23 were in hibernation at the time of capture. Badgers retrieved the majority (14 of 18 available) of carcasses, with the latest retrieval occurring on 9 December. Carcasses were retrieved, in the order they were cached, 1 to 55 days (X̄ = 14 days) after caching. Cache storage, cache retrieval, and consumption of freshly caught prey were prevalent in autumn, often occurring on the same night, indicating that caching contributed to fattening in autumn rather than as a food reserve to be used by badgers during winter.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.162
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.159
GPT teacher head0.240
Teacher spread0.081 · 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
Published2000
Admission routes1
Has abstractyes

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