Dog breeds and conformations in the UK in 2019: VetCompass canine demography and some consequent welfare implications
Bibliographic record
Abstract
INTRODUCTION: Growing concerns over health and welfare impacts from extreme phenotypes in dogs have created an urgent need for reliable demographic information on the national breed structures of dogs. METHODS: This study included all dogs under primary veterinary care in the UK during 2019 at practices participating in VetCompass. Demographic data on these dogs were analysed to report on the frequency of common breeds and also to report on conformation, bodyweight, sex and neuter associations with these breeds. RESULTS: The study included 2,237,105 dogs under UK veterinary care in 2019. Overall, 69.4% (n = 1,551,462) were classified as purebred, 6.7% (149,308) as designer-crossbred and 24.0% (536,335) as nondesigner-crossbred. Across 800 unique breed names, the most frequent breeds at any age were nondesigner-crossbred (n = 536,335, 24.0%), Labrador Retriever (154,222, 6.9%) and Jack Russell Terrier (101,294, 4.5%). Among 229,624 (10.3%) dogs aged under one year, the most frequent breeds were nondesigner-crossbred (n = 45,995, 20.0%), French Bulldog (16,036, 7.0%) and Cockapoo (14,321, 6.2%). Overall, based on breed characteristics, 17.6% (395,739) were classified as brachycephalic, 43.1% (969,403) as mesaticephalic and 8.3% (186,320) as dolichocephalic. Of 1,551,336 dogs that were classifiable based on breed, 52.6% (815,673) were chondrodystrophic. Of 1,462,925 dogs that were classifiable, there were 54.6% (n = 798,426) short haired, 32.6% (476,883) medium haired and 12.8% (186,934) long haired. Of 1,547,653 dogs that were classifiable for ear carriage, 24.5% (n = 379,581) were erect, 28.1% (434,273) were semi-erect, 19.7% (305,475) were v-shaped drop and 27.7% (428,324) were pendulous. Overall, there was a 1.09:1.00 ratio of male (n = 1,163,512; 52.2%) to female dogs (n = 1,067,552; 47.8%). CONCLUSIONS: Health and welfare issues linked to popular breeds with extreme phenotypes suggest that there is much work to do to help owners to make more welfare-friendly decisions when choosing which type of dog to own.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".