Best in show but not best shape: a photographic assessment of show dog body condition
Bibliographic record
Abstract
Previous studies suggest that owners often wrongly perceive overweight dogs to be in normal condition. The body shape of dogs attending shows might influence owners' perceptions, with online images of overweight show winners having a negative effect. This was an observational in silico study of canine body condition. 14 obese-prone breeds and 14 matched non-obese-probe breeds were first selected, and one operator then used an online search engine to identify 40 images, per breed, of dogs that had appeared at a major national UK show (Crufts). After images were anonymised and coded, a second observer subjectively assessed body condition, in a single sitting, using a previously validated method. Of 1120 photographs initially identified, 960 were suitable for assessing body condition, with all unsuitable images being from longhaired breeds. None of the dogs (0 per cent) were underweight, 708 (74 per cent) were in ideal condition and 252 (26 per cent) were overweight. Pugs, basset hounds and Labrador retrievers were most likely to be overweight, while standard poodles, Rhodesian ridgebacks, Hungarian vizslas and Dobermanns were least likely to be overweight. Given the proportion of show dogs from some breeds that are overweight, breed standards should be redefined to be consistent with a dog in optimal body condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".