Obesity risk factors in British Labrador retrievers: Effect of sex, neuter status, age, chocolate coat colour and food motivation
Bibliographic record
Abstract
BACKGROUND: Canine obesity is a complex disease affected by genetic, endocrine and environmental factors. It is associated with reduced lifespan and many comorbidities. Prevalence differs by breed, with Labrador retrievers at high risk. Past data on how biological risk factors impact weight gain have been contradictory, possibly because they were obtained from genetically heterogeneous populations. METHODS: We investigated risk factors for canine obesity in a population of British Labrador retrievers (n = 521) with high-quality data on obesity, weight, owner-reported food motivation and related characteristics. We used linear regression to assess known and novel risk factors for obesity. RESULTS: We found that neutering increased obesity in males (p < 0.001) but not females (p = 0.37). Older age was associated with obesity in female Labradors (p = 0.013) but not males (p = 0.49). We identified two new risk factors for obesity in Labrador retrievers: chocolate coat colour (p < 0.001) and high food motivation (p < 0.001). LIMITATIONS: Strategic recruitment to collect both obese and lean dogs means this cohort is not suitable for assessing obesity prevalence in UK Labrador retrievers. CONCLUSION: Studying this genetically homogeneous population informs our knowledge of common risk factors for obesity and expands those relevant to Labrador retrievers.
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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".