Prévalence et facteurs de risque de l'obésité et du surpoids chez le chien : enquête en Australie et comparaison avec la situation en France
Bibliographic record
Abstract
Between January and March 2017, a survey was conducted in the city of Sydney, Australia in two private veterinary clinics. The purpose of this survey was to gather information around the presumed risk factors of obesity and overweightedness in dogs (the owner’s profile, the dog’s way of living, profile, diet, and physical activity, and the owner’s perception of his dog’s body condition, as well as his relationship with him). A total of 426 privately owned dogs were covered by the survey. Statistical analyses of the results from 302 questionnaires were conducted to identify the prevalence, as well as risk factors, of obesity and overweightedness in the Australian canine population. The analysis results were then compared to those of a similar study found in the literature, highlighting the similarities and differences between them. In summary, the results from the dog population surveyed showed that dogs displaying the following attributes were more prone to becoming obese or overweight: having a dog aged 8 to 11, being a Labrador or Pug, being neutered, displaying a gluttonous behavior towards food, sick from allergies or endocrine disorders, not receiving any exercise, not going out for walks, receiving treats, fed ad-limitum, fed less than three times a day, living in a studio apartment, and those considered as a child or part of the family. The comparison between a French study conducted in France in 2003 in the Ecole Nationale Veterinaire d’Alfort, and this Australian study indicated certain risk factors act in the same way towards the overweight nature of dogs: the dog age, the neutered status and whether they are of pure or crossed breed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".