Impact of social and personalized visual enrichment on stereotypic behaviors in rhesus macaque monkeys in the laboratory: a case study
Bibliographic record
Abstract
Stereotypies, defined as repetitive behaviors with no apparent purpose, are frequent in some animals kept in captivity, posing a major ethical and scientific challenge for biomedical research involving animals for scientific purposes. This article describes the case of a rhesus macaque who tends to express marked stereotypies when temporarily separated from his partner. In an attempt to improve the animal's condition during these short periods of separation, the impact of exposure to a video of his partner was tested. This video was shown to the animal during five experimental sessions, and the animal's behavior was analyzed using BORIS software, which quantifies the duration of stereotypies. The results indicate an immediate and significant reduction in stereotypic behaviors when the animal is first exposed to the video of his partner during her absence. However, a gradual return of stereotypies is observed during subsequent exposures, suggesting habituation to the visual enrichment offered. A lasting improvement in well-being through social visual enrichment is therefore not observed for this animal. These data are discussed in light of the specificity of the protocol used, the factors that may have interfered with its effectiveness, and the profile of the animal in question.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".