Rats as pets: Predictors of adoption and surrender of pet rats (Rattus norvegicus domestica) in British Columbia, Canada
Bibliographic record
Abstract
Whereas much research has been conducted on rats in their roles as pests and laboratory animal models, little is known about rats in their role as companion animals. However, rats have become the third most common companion animal admitted to the British Columbia Society for the Prevention of Cruelty to Animals (BC SPCA) shelter system after cats and dogs. This paper analyses 5 years of province-wide rat admission and outcome data (n = 3,392) at the BC SPCA. Most rats that entered BC SPCA shelters were white, sexually intact, and pups less than 6 months old. Rats were mostly relinquished by their owners, and the most common surrender reasons were due to owner-related issues and housing issues. Reasons for euthanasia were primarily poor health and neonatal age. A multiple linear regression model found that rats that were either senior, albino, unhealthy, seized by humane officers, or born onsite tended to stay longer in shelters (F[12, 1466] = 9.565, p < .001, adjusted R2 = .06). Time to adoption for albino rats was 79% longer than for white rats. These findings help us understand the preferences of rat adopters and why the rat-human relationship may fail. Results may also be useful to improve the quality of life for pet rats by identifying programs to reduce their length of stay in animal shelters. Finally, our study highlights new questions for welfare research in an understudied companion animal-the pet rat.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".