Activity budget and spatial distribution of Bennett's wallabies (<i>Macropus rufogriseus</i>) in open versus closed exhibit designs
Bibliographic record
Abstract
Although many studies investigating the impacts of zoo exhibit designs on captive animals exist, none have been performed on how they influence the behavior and welfare of captive Bennett's wallabies (Macropus rufogriseus). Here, we assess the impact of exhibit design on the activity budget and spatial distribution of Bennett's wallabies. We compared animal behavior in two open exhibits (i.e. physical interaction between animals and visitors permitted) to two closed exhibits (i.e. physical interaction between animals and visitors prohibited). Behavioral data were collected using focal sampling, and spatial distribution was recorded on exhibit maps at regular time intervals. We found a significant increase in feeding and interactive behaviors in closed exhibits in comparison to open exhibits. However, other behaviors such as resting, locomotion, and vigilance did not vary with design. Functional use of space was similar between both designs; however, the effect of habituation may be relevant to consider in future studies. Although some support for visitor effects were present, our study provided no evidence for strong impacts of exhibit design on Bennett's wallaby welfare. Our study emphasizes the need for additional research into the impacts of how zoo environments affect Bennett's wallaby behavior and welfare.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".