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Record W2157775870 · doi:10.1111/aje.12207

Decline of sable antelope in one of its key conservation areas: the greater<scp>H</scp>wange ecosystem,<scp>Z</scp>imbabwe

2015· article· en· W2157775870 on OpenAlexaff
William‐Georges Crosmary, Simon Chamaillé‐Jammes, Godfrey Mtare, Hervé Fritz, Steeve D. Côté

Bibliographic record

VenueAfrican Journal of Ecology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité Laval
FundersAgence Nationale de la Recherche
KeywordsNational parkWildlifeHabitatGeographyEcosystemCompetition (biology)EcologyForageAfrican elephantWildlife conservationPredationBiology

Abstract

fetched live from OpenAlex

Abstract Land use has major effects on wildlife conservation. We studied variations of sable antelope H ippotragus niger densities between 1990 and 2001 in comparison with various land uses in and around Hwange National Park, Zimbabwe. Trends of other ungulates, including elephant Loxodonta africana , were examined simultaneously, because sable may be sensitive to forage and apparent competition and to high elephant densities. Sable densities declined in the whole region, very likely because of adverse rainfall conditions. Densities were constantly higher in the hunting areas and forestry lands than in the national park. Interestingly, elephant densities showed the opposite, with higher densities in the national park than in the adjacent areas. Whether these results reflect a negative effect of high elephant numbers on sable must still be tested directly. Likewise, while habitat characteristics and lion predation did not appear responsible for the higher sable densities outside the national park, they could not be discounted as an influence on the differing sable densities in different land‐use areas. It is clear, however, that high protection status is not always sufficient to ensure adequate conservation of flagship species. We therefore call for further investigations of ecological interactions within protected areas.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.230
Teacher spread0.194 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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