MétaCan
Menu
Back to cohort

Effect of raven Corvus corax scavenging on the kill rates of wolf Canis lupus packs

2005· article· en· W2178349151 on OpenAlexaffabout
Petra Kaczensky, Robert D. Hayes, Christoph Promberger

Bibliographic record

VenueWildlife Biology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsYukon Department of Environment
Fundersnot available
KeywordsPredationCanisScavengingCarrionCorvidaeEcologyCompetition (biology)BiologyZoologyAnimal science

Abstract

fetched live from OpenAlex

During late winter 1991 and 1992, we investigated the influence of raven Corvus corax scavenging on the predation rate by different-sized wolf Canis lupus packs on moose Alces alces in the Yukon Territory, Canada. To assess the magnitude of scavenging, we presented 10 ungulate carcasses, pre-warmed to simulate the flesh temperature of freshly-killed prey, to scavengers and measured their daily consumption. Ravens were by far the main scavengers and on average, we counted 18.5 ± 12.7 (SD) ravens and documented removal of 14.1 ± 1.3 (SE) kg biomass each day (N = 53 observation days). However, assuming a daily scavenging rate of 14 kg by ravens fails to explain the almost equally short handling times for moose carcasses of small, medium and large packs. Only when raven consumption rate varies with pack size can we match the observed pattern. Assuming complete consumption, daily raven scavenging has to be 43 kg for ravens feeding on the kills of small wolf packs, 21 kg for ravens feeding on the kills of medium packs and close to zero for ravens feeding on the kills of large packs. Thus raven-wolf competition is highest for small packs, where ravens manage to remove up to 75% of the edible biomass and very low for large packs where ravens hardly manage to remove any edible biomass. Large packs seem to leave less opportunity for ravens to feed on carcasses, possibly because some wolves are always present at the kill and either actively chase away ravens or inhibit access to the carcass.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueWildlife BiologySame topicWildlife Ecology and ConservationFrench-language works237,207