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Record W2598431539 · doi:10.22621/cfn.v130i4.1930

Observation of an Eastern Wolf (<i>Canis</i> sp. cf. <i>lycaon</i>) Caching Food in a Sphagnum Bog in Algonquin Provincial Park, Ontario

2017· article· en· W2598431539 on OpenAlexaffvenueabout
Patrick D. Moldowan, Hugo Kitching

Bibliographic record

VenueThe Canadian Field-Naturalist · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of TorontoAlgonquin College
Fundersnot available
KeywordsBogOdocoileusPredationSphagnumEcologyGeographyCanisCarrionBiology

Abstract

fetched live from OpenAlex

We report summer caching of a partial carcass of a White-tailed Deer (Odocoileus virginianus) fawn by an Eastern Wolf (Canis sp. cf. lycaon) in a Sphagnum bog in Algonquin Provincial Park, Ontario, Canada. The microhabitat conditions in bogs (i.e., low temperature, acidity, and organochemical compounds) likely inhibit food spoilage, making bogs potentially important sites for food caching. Wolves in Algonquin Park experience low summer food availability and high pup mortality from starvation. Caches likely serve as necessary reserve food stores for adults and pups. Recent research has shown that wetland habitats are important den and rendezvous sites for Algonquin Eastern Wolves based on prey availability and, we suggest, perhaps for food storage and accessibility. This caching behaviour was recorded on video. We recommend that future research investigate Eastern Wolf selection of food-caching sites, as a complement to other spatial ecology studies.

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 score0.320
Threshold uncertainty score0.643

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.018
GPT teacher head0.221
Teacher spread0.203 · 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

Citations2
Published2017
Admission routes3
Has abstractyes

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