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Record W2166655717 · doi:10.1139/z09-082

A keystone predator at risk? Density and distribution of the spotted hyena (Crocuta crocuta) in the Etosha National Park, Namibia

2009· article· en· W2166655717 on OpenAlexvenueno aff
Martina Trinkel

Bibliographic record

VenueCanadian Journal of Zoology · 2009
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCrocuta crocutaHyenaWildebeestBiologyEquusPantheraPredationEcologyZoologyNational parkPopulationAcinonyx jubatusPopulation densityDemography

Abstract

fetched live from OpenAlex

For wildlife management and conservation biology, it is important to be able to estimate the status and distribution of animals and to monitor their population trends. In the Etosha National Park, Namibia, there is a lack of knowledge about numbers and distribution of spotted hyenas ( Crocuta crocuta (Erxleben, 1777)) and factors regulating their population. To estimate hyena density and distribution, tape-recorded vocalizations (call-ups) were performed to attract hyenas in the central and eastern parts of Etosha. Eighty-five adult and subadult hyenas responded to the calls, with most of them responding in an area with high density of migratory ungulates, principally springbok ( Antidorcas marsupialis (Zimmermann, 1780)), wildebeest ( Connochaetes taurinus (Burchell, 1823)), and Burchell’s zebra ( Equus burchelli (Gray, 1824)). These migratory species are the main prey of spotted hyenas in Etosha. There was a strong spatial relationship between hyena density and migratory prey biomass. Based on this mathematical correlation, I estimated 203 ± 79 hyenas, i.e., 2.7 ± 1.1 hyenas/100 km 2 , in the central and eastern parts of Etosha. Applying this correlation to the western part of the park, it was possible to estimate 339 ± 176 spotted hyenas, corresponding to an overall density of 2.1 ± 1.0 hyenas/100 km 2 , in the whole Etosha National Park.

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 score0.085
Threshold uncertainty score0.928

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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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

Citations28
Published2009
Admission routes1
Has abstractyes

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