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Record W2045097353 · doi:10.1139/z06-147

Feeding habits and trophic niche overlap between sympatric golden jackal (<i>Canis aureus</i>) and red fox (<i>Vulpes vulpes</i>) in the Pannonian ecoregion (Hungary)

2006· article· en· W2045097353 on OpenAlexvenueno aff
József Lanszki, Miklós Heltai, László Szabó

Bibliographic record

VenueCanadian Journal of Zoology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsJackalVulpesBiologyWild boarEcologyPredationTrophic levelZoologySympatric speciation

Abstract

fetched live from OpenAlex

The feeding ecology of the golden jackal ( Canis aureus L., 1758) and its interspecific trophic relationship with the sympatric red fox (Vulpes vulpes (L., 1758)) was investigated in an area of recent range expansion of the golden jackal in Hungary, central Europe. Diet composition was determined by scat analysis (over 4 years: jackal 814 scats; fox 894 scats). Compared with jackals, foxes consumed more small mammals (mean biomass consumed: jackal 77%; fox 68%) and to a lesser extent plant matter (6% and 18%, respectively). The importance of other prey, such as wild boar ( Sus scrofa L., 1758), cervids, brown hare ( Lepus europaeus Pallas, 1778), birds, reptiles, fish, invertebrates, and domestic animals, was minimal. Both mesocarnivores consumed primarily small animals (&lt;50 g: 92% and 87%, respectively); this implies a typical searching and solitary hunting strategy. The trophic niche breadth of both species was very narrow and the fox proved to be more of a generalist. The food overlap index between the two canids was high (mean, 73%) and varied with the decreasing availability and consumption of small mammals. Based on prey remains found in scats, small-mammal specialization over a 2-year period and seasonal predation upon wild boar piglets (mainly by the jackal), seasonal fruit eating (mainly by the fox), and scavenging on wild or domestic ungulates (both predators) were found.

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 score0.148
Threshold uncertainty score0.996

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)0.0000.000

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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

Citations119
Published2006
Admission routes1
Has abstractyes

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