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Record W2105912728 · doi:10.14430/arctic699

Foraging Behaviours of Wolverines at a Large Arctic Goose Colony

2002· article· en· W2105912728 on OpenAlexafffundvenueabout
Gustaf Samelius, Ray T. Alisauskas, Serge Larivière, Christoffer Bergman, Christopher J. Hendrickson, Kimberly Phipps, Credence Wood

Bibliographic record

VenueARCTIC · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsPrince Albert Grand CouncilSaskatchewan PolytechnicUniversity of Saskatchewan
FundersUniversity of SaskatchewanMassachusetts Department of Fish and GameCalifornia Department of Fish and Game
KeywordsGooseMustelidaeForagingGeographyEcologyZoologyBrantaAnatidaeBiologyFishery

Abstract

fetched live from OpenAlex

At the large Ross's goose and lesser snow goose colony at Karrak Lake, Nunavut, Canada, we saw wolverines kill two geese, take 13 eggs from 12 goose nests, and take three goose carcasses from two fox dens. Wolverines also made unsuccessful attempts to capture geese and frequently ignored eggs from nests where geese had fled the approaching wolverine. Most foods (all geese killed by wolverines and 80% of the eggs) were cached for later use, whereas few foods were eaten immediately (20% of the eggs and part of a goose taken from a fox den, which was later lost) or lost (all geese taken from fox dens). Wolverines spent little time caching foods (e.g., some foods were never covered), which suggests that recovery of these foods was not crucial to wolverines. When taking foods from fox dens, wolverines were mobbed by foxes; as a result, only one wolverine managed to consume part of a goose carcass taken from a fox den. These observations illustrate the opportunistic nature of wolverines and suggest that their scavenging success may be influenced by how well foods are defended.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.993

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)0.0080.001

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 head0.014
GPT teacher head0.213
Teacher spread0.200 · 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.

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

Citations26
Published2002
Admission routes4
Has abstractyes

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