Overweight in Swedish show dogs–prevalence and association with performance in competition
Bibliographic record
Abstract
BACKGROUND: The prevalence of overweight and obesity is increasing in companion dogs, but little is known of these conditions in show dogs. This study assessed body condition score (BCS) of show dogs of six selected popular breeds at a major Swedish dog show event and examined the association between BCS and performance in competition. RESULTS: At one of Sweden's largest dog shows, BCS of 120 dogs of six different breeds was assessed by trained animal healthcare personnel, using a 9-point BCS scale with conditional cut-off for overweight set to BCS ≥ 6. Prevalence of overweight in the cohort was 32% but all overweight dogs except one displayed only slight overweight (BCS 6) and no dog was assessed as obese (BCS 8-9). Prevalence of overweight differed significantly between breeds (P < 0.0001) with Labrador retrievers, Golden retrievers and French bulldogs showing the highest mean BCS (5.6-5.7) and highest prevalence of overweight (50-67%). Lean and overweight dogs received awards and higher show awards (certificates) to the same extent, and no significant association between slight overweight and performance in competition was found. CONCLUSIONS: Prevalence of overweight in Swedish show dogs was relatively high and in the same range as in the Swedish dog population as a whole. Dog owners, breeders and judges should be made aware of canine obesity problems and trained in BCS assessment, to better prevent canine overweight and associated health risks. This is particularly important for retriever and brachycephalic breeds, which showed high prevalence of slight overweight and have breed-specific health problems exacerbated by overweight. Owners and breeders of traditionally sturdy dog breeds should be informed that overweight dogs do not outperform lean dogs in competition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".