The Welfare and Educational Impacts of Encounter Experiences and Displays on Zoo‐Housed Red Panda ( <i>Ailurus fulgens</i> )
Bibliographic record
Abstract
Close animal encounters potentially increase visitor connection to species and present an educational and fundraising opportunity. However, evidence of the impacts on animal welfare or visitor education is limited. Red panda (Ailurus Fulgens spp.) encounters are gaining popularity despite a lack of research on their effects. As red panda are a characteristically cautious species and prone to disturbance, concern has been raised as to their suitability for encounters. We examined the extent and composition of red panda encounters amongst 150 Global Species Management Plan (GSMP) member zoos (survey responses), and their impact on longevity and reproduction (species 360 analysis). Over a third (39%) of zoos surveyed offered red panda encounters, with most (71%) being animal feeding experiences. Educational information was provided in almost all cases (95%) and focused on the encounter individuals and species' natural history. Of the 31 encounter red panda who were also part of a breeding program, 24 reproduced. Comparative data analysis suggested that encounter red panda produced more offspring and had higher longevity (survival) than non-encounter individuals, although this may reflect changes in red panda husbandry over time. A. f. styani were less likely to breed and produced fewer offspring than A. f. fulgens. Whilst there appears to be no major negative impacts of red panda encounters, continued monitoring and ensuring high animal-welfare standards remains vital.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".