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Record W4312179689 · doi:10.1111/acv.12845

Examining the representation of locally threatened species in North American zoos

2022· article· en· W4312179689 on OpenAlexaff
Kevin C. R. Kerr, Drew Sauve, S. A. Winton, Toby J. Thorne, A. A. Chabot

Bibliographic record

VenueAnimal Conservation · 2022
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsQueen's UniversityUniversity of GuelphWildlife Conservation Society CanadaToronto Zoo
Fundersnot available
KeywordsThreatened speciesNear-threatened speciesConservation-dependent speciesIUCN Red ListEndangered speciesConservation statusEcologyPopulationGeographyBiologyHabitatDemography

Abstract

fetched live from OpenAlex

Abstract A perceived dissonance exists between the stated conservation mission of modern zoos and the composition of species in their care. A bias toward charismatic taxa over threatened ones has been demonstrated in several studies. However, these prior examinations have principally relied on species threat status derived from global lists, and threat status reported for species on national and subnational lists often differ from global assessments. It has been suggested that some globally non‐threatened species maintained in zoos might fulfill local conservation priorities. In this study, we compare local threat status and zoo population size for native bird, mammal, reptile and amphibian species in North American zoos to assess the representation of locally threatened taxa. We found that native locally threatened species were kept in North American zoos slightly less than would be expected by chance; however, of the species that were represented in zoos, those that were threatened in more of the subnational regions where they occurred averaged larger populations within zoos. This suggests that the direct contribution from zoo animals to the conservation of locally threatened species is relatively limited with respect to taxonomic coverage, but this contribution may be underestimated by strictly counting the threatened species kept in zoos.

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)0.000
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 score0.011
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.114
GPT teacher head0.324
Teacher spread0.210 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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