Examining the representation of locally threatened species in North American zoos
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".