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Record W2144503057 · doi:10.1111/conl.12182

Another Continental Vulture Crisis: Africa's Vultures Collapsing toward Extinction

2015· article· en· W2144503057 on OpenAlexaff
Darcy Ogada, Phil Shaw, René Beyers, Ralph Buij, Campbell Murn, Jean Marc Thiollay, Colin M. Beale, Ricardo M. Holdø, Derek Pomeroy, Neil Baker, Sonja Krüger, André Botha, Munir Z. Virani, Ara Monadjem, A. R. E. Sinclair

Bibliographic record

VenueConservation Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversity of British Columbia
FundersRoyal SocietyPeregrine FundAfrican Bird Club
KeywordsVultureCritically endangeredEndangered speciesGeographyExtinction (optical mineralogy)EcosystemEcologyThreatened speciesLegislationEnvironmental protectionBiologyHabitatPolitical science

Abstract

fetched live from OpenAlex

Vultures provide critical ecosystem services, yet populations of many species have collapsed worldwide. We present the first estimates of a 30-year Pan-African vulture decline, confirming that declines have occurred on a scale broadly comparable with those seen in Asia, where the ecological, economic, and human costs are already documented. Populations of eight species we assessed had declined by an average of 62%; seven had declined at a rate of 80% or more over three generations. Of these, at least six appear to qualify for uplisting to Critically Endangered. Africa's vultures are facing a range of specific threats, the most significant of which are poisoning and trade in traditional medicines, which together accounted for 90% of reported deaths. We recommend that national governments urgently enact and enforce legislation to strictly regulate the sale and use of pesticides and poisons, to eliminate the illegal trade in vulture body parts, as food or medicine, and to minimize mortality caused by power lines and wind turbines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.063
GPT teacher head0.257
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 source (direct Gemma or distilled Codex), 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

Citations400
Published2015
Admission routes1
Has abstractyes

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