Enrichment and animal age, not biological variables, predict positive welfare indicators in zoo-housed carnivores
Bibliographic record
Abstract
The development of evidence-based zoo animal welfare science and the use of the 'five domains' have inspired zoos to increase animal welfare, particularly recognising positive welfare states. We tested whether natural biology (number of habitats, latitudinal range, sociality, body weight) or husbandry variables (mean age of group, group size and presence of extra enrichment) predict rates of positive welfare indicators (activity, play and engagement with the environment) in the Order Carnivora from collecting data from previously published articles. For each behaviour, species (n=23) medians were analysed using phylogenetically informed mixed-model regression. Activity data were from 136 animals (n=23 species), environmental interaction from 55 animals (n=15 species) and play from 27 animals (n=7 species). Biological variables did not predict rates of behaviour at a species or an individual animal level, but husbandry variables did. At an individual level, activity and play decreased in older animals. Activity and interaction with environment also increased with additional enrichment. This study is the first to quantify positive behaviours performed by zoo housed Carnivora and shows that they display indicators of positive welfare, if appropriate husbandry including environmental enrichment is provided.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".