Applying GPS and Accelerometers to the Study of African Savanna (Loxodonta africana) and Asian Elephant (Elephas maximus) Welfare in Zoos
Bibliographic record
Abstract
African savanna elephants (Loxodonta africana) and Asian elephants (Elephas maximus) are a focus of welfare research in zoos due to their high intelligence, complex social structure, and sheer size. Due to these challenges, some argue that zoos are inherently incapable of providing appropriate care for elephants, while others believe that zoos can fulfill the needs of these species with improved husbandry. There is a general consensus from both within and outside of zoos, however, that zoos must improve their elephant programs or cease exhibiting these animals altogether. Now more than ever, applied research on zoo elephant welfare is needed to provide context for this debate. Researchers are interested in how far zoo elephants walk due to the potential health and welfare benefits of walking in these highly mobile species. Zoo researchers recently adopted GPS technology to study elephant walking, and preliminary evidence suggests that African elephants in large zoo exhibits walk distances that correspond with wild elephants under non-extreme conditions. However, data are limited from Asian elephants and from elephants in more typically-sized exhibits. In Chapter Two, I discuss important methodological considerations of utilizing GPS in a zoo environment, including an introduction to the technology, sources of error and mitigation, methods to improve GPS performance, and possible effects of GPS device attachment on animal behavior. This review shows GPS performance is adequate for tracking zoo elephant walking when proper methodological techniques are applied, and should serve as a useful reference for zoo researchers considering using GPS. In Chapter Three, I used GPS anklets to measure outdoor daily walking distance in 56 adult female African (n = 33) and Asian (n = 23) elephants housed in 30 zoos. I collected 259 days of data and found that elephants walked an average of 5.34 km/day with no significant difference between species. Multivariate regression models predicted that elephants with more dynamic feeding regimens (more diverse feeding types and frequencies; unscheduled feeding times) will walk more. Distance walked was also predicted to be higher in elephants that spend time in a greater number of different social groups. Distance walked was predicted to decline with age. Finally, I found a significant negative correlation between distance walked and nighttime space experience. The results of the analysis suggest that zoos that want to increase walking in their elephants need not rely solely on larger exhibits, but can increase walking by adding quality and complexity to exhibits. However,
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".