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Record W2001124188 · doi:10.1890/100172

From venison to beef: seasonal changes in wolf diet composition in a livestock grazing landscape

2011· review· en· W2001124188 on OpenAlexafffundabout
Andrea T. Morehouse, Mark S. Boyce

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2011
Typereview
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Alberta
FundersParks CanadaAlberta Beef ProducersAlberta Conservation Association
KeywordsLivestockGrazingPredationGeographyEcologyCattle grazingBiology

Abstract

fetched live from OpenAlex

Wild ungulates are the primary prey for wolves in North America, but livestock predation is a concern in areas where wolves and livestock overlap. Using clusters of global positioning system telemetry relocations and scat analysis, we investigated wolf diets year‐round in southwestern Alberta, where seasonal cattle grazing is the predominant land use and wolf–cattle conflicts have increased in recent years. Both methods indicated a seasonal shift in wolf diets, from wild prey during the non‐grazing season to cattle in the grazing season. Wolves scavenged more frequently during the non‐grazing season than during the grazing season; 85% of all scavenging events occurred at ranchers' boneyards (where livestock carcasses are dumped), where wolves fed on dead livestock. Cattle represent a higher proportion of wolf diets than previously thought; we recommend the sanitary disposal of dead livestock to prevent wolves from becoming accustomed to feeding on livestock, and the development of management plans aimed at reducing predation on cattle if humans and wolves are to coexist on landscapes that are dominated by livestock ranching.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.250
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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
GenreReview

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

Citations62
Published2011
Admission routes3
Has abstractyes

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