Perceptions of Veterinarians in British Columbia of Cat Management Strategies to Reduce Cat Overpopulation and Impacts on Wildlife Populations
Bibliographic record
Abstract
Free-roaming cats present a multidisciplinary challenge for wildlife conservation and feline welfare. Veterinary professionals are critical stake-holders in the issue of free-roaming cats, making it imperative that their attitudes and perspectives are well understood. In this study, we conducted an electronic survey of veterinarians practicing across British Columbia to evaluate their perceptions of 1) whether cat overpopulation is a concern, 2) the impacts of cats on wildlife, and 3) alternative management strategies for dealing with cat overpopulation. Of the 389 respondents, 56% agreed that cat overpopulation was a local problem, and 91% agreed that veterinarians play a role in reducing cat overpopulation. Veterinarians in rural communities had a greater involvement in cat overpopulation and perceived it as more problematic than their urban colleagues. The majority (70%) of veterinarians reported that they performed early-age sterilization, supported the principle of low-cost spay-neuter services (67%), and 65% of them were involved in these services. Just over half (54%) of respondents reported that they provide permanent identification to cats, typically offering both microchips and tattoos. Veterinarians were less cohesive on their perceptions of the impact of free-roaming cats on wildlife: 53% agreed that after habitat loss cats are one of the most significant, direct causes of bird and wildlife mortality, while 30% were neutral and 17% disagreed. Fifty-six percent of veterinarians felt that keeping cats indoors would be one of the most effective strategies to reduce cat impacts on wildlife, but they also felt that public education programs and low-cost spay-neuter programs would be effective and more popular with cat owners. Most veterinarians (79%) recommended against unsupervised outdoor access, citing safety and disease risks. Rodent control was the most common reason for not recommending an indoor lifestyle. Overall, our survey results suggest that veterinarians are committed participants in the prevention of cat overpopulation. Conservationists need to focus on increased engagement with veterinarians regarding the impact of cats on wildlife.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".