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Record W2177435077 · doi:10.1644/bns-102

HUNTING TECHNIQUES AND TOOL USE BY NORTH AMERICAN BADGERS PREYING ON RICHARDSON'S GROUND SQUIRRELS

2004· article· en· W2177435077 on OpenAlexaffabout
Gail R. Michener

Bibliographic record

VenueJournal of Mammalogy · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsBurrowBadgerPredationGeographyGround squirrelHibernation (computing)JuvenileEcologyHabitatBiology

Abstract

fetched live from OpenAlex

Techniques used by North American badgers (Taxidea taxus) when hunting Richardson's ground squirrels (Spermophilus richardsonii) were assessed over a 15-year period in southern Alberta to determine the relationship between activity of prey and methods used to capture prey. Badgers frequently hunted hibernating squirrels in autumn, sometimes hunted infants in spring, and rarely hunted active squirrels in summer. Badgers always captured hibernating squirrels and infants underground, usually captured active squirrels underground, and sometimes intercepted fleeing squirrels aboveground. Regardless of season or year, the most common hunting technique used by badgers was excavation of burrow systems, but plugging of openings into ground-squirrel tunnels accounted for 5–23% of hunting actions in 4 consecutive years. Plugging occurred predominantly in mid-June to late July before most ground squirrels hibernated and in late August to late October when juvenile males were active but other squirrels were in hibernation. Badgers usually used soil from around the tunnel opening or soil dragged 30–270 cm from a nearby mound (72% and 22% of 391 plugged tunnels, respectively) to plug tunnels. The least common (6%), but most novel, form of plugging used by 1 badger involved movement of 37 objects from distances of 20–105 cm to plug openings into 23 ground-squirrel tunnels on 14 nights. Aimed movement of objects to plug openings into burrow systems occupied by ground squirrels qualified this badger as a tool user.

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 score0.021
Threshold uncertainty score0.380

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 head0.012
GPT teacher head0.240
Teacher spread0.229 · 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

Citations54
Published2004
Admission routes2
Has abstractyes

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