Benchmarking Enrichment Efforts in the US & Canada Across Species and Enrichment Categories
Bibliographic record
Abstract
Enrichment is important for animal welfare and data quality. Provision of enrichment opportunities varies between species and enrichment category. However, data benchmarking these differences does not exist. Our objective was to characterize enrichment provision and associated factors across species in the US and Canada. Personnel who work with research animals (n = 1098) in the US and Canada voluntarily responded to online promotions and completed a survey about enrichment used for the species they worked with most, their control of and wish for more enrichment, stress or pain in the animals they worked the most with, and demographics. All participants (except those working with rats) received the same questionnaire regardless of species to allow objectivity, as the effects of many enrichment items on some species have not yet been determined. The questionnaire asked about enrichments that were beneficial to at least one species. The provision of enrichment was allocated into 2 outcome variables: diversity and frequency per enrichment category. Results showed a significant interaction between enrichment category and species. Generally, physical, nutritional, and sensory enrichments were provided less often than social enrichment. In addition, nonhuman primates received more diverse and more frequent enrichment than did other species (twice as much as rats and mice). Enrichment was provided less frequently by personnel who wished they could do more than the status quo. Both enrichment frequency and diversity were higher in respondents from Canada, those who had more control over provision, and those who had been in the field longer. While our results cannot be used to determine the quality of enrichment provided to various species, they do provide information on current enrichment practices in the US and Canada and identify differences in implementation by species and enrichment category. The data also indicate provision of enrichment is influenced by factors such as country and individual control over enrichment. This information can also be used to identify areas for greater enrichment efforts for some species (for example, rats and mice) and categories, with the ultimate goal of improving animal 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".