Estimation of the number and demographics of companion dogs in the UK
Bibliographic record
Abstract
BACKGROUND: Current estimates of the UK dog population vary, contain potential sources of bias and are based on expensive, large scale, public surveys. Here, we evaluate the potential of a variety of sources for estimation and monitoring of the companion dog population in the UK and associated demographic information. The sources considered were: a public survey; veterinary practices; pet insurance companies; micro-chip records; Kennel Club registrations; and the Pet Travel Scheme. The public survey and subpopulation estimates from veterinary practices, pet insurance companies and Kennel Club registrations, were combined to generate distinct estimates of the UK owned dog population using a Bayesian approach. RESULTS: We estimated there are 9.4 (95% CI: 8.1-11.5) million companion dogs in the UK according to the public survey alone, which is similar to other recent estimates. The population was judged to be over-estimated by combining the public and veterinary surveys (16.4, 95% CI: 12.5-21.5 million) and under-estimated by combining the public survey and insured dog numbers (4.8, 95% CI: 3.6-6.9 million). An estimate based on combining the public survey and Kennel Club registered dogs was 7.1 (95% CI: 4.5-12.9) million. Based on Bayesian estimations, 77 (95% CI: 62-92)% of the UK dog population were registered at a veterinary practice; 42 (95% CI: 29-55)% of dogs were insured; and 29 (95% CI: 17-43)% of dogs were Kennel Club registered. Breed demographics suggested the Labrador was consistently the most popular breed registered in micro-chip records, with the Kennel Club and with J. Sainsbury's PLC pet insurance. A comparison of the demographics between these sources suggested that popular working breeds were under-represented and certain toy, utility and miniature breeds were over- represented in the Kennel Club registrations. Density maps were produced from micro-chip records based on the geographical distribution of dogs. CONCLUSIONS: A list containing the breed of each insured dog was provided by J. Sainsbury's PLC pet insurance without any accompanying information about the dog or owner.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".