113 Owner Dietary and Exercise Regimens Influence Perception of Ideal Body Weight in Dogs
Bibliographic record
Abstract
Abstract The increasing prevalence of canine obesity across the globe has become the number one health concern for dogs. Part of the problem may be the way owner’s perceive their dog’s body weight. The goal of the current survey was to assess what variables, related to both owner and dog’s feeding and exercising practices, were predictive of the owner’s perception of their dog’s body weight across North America (Canada and the United States) and Europe (France, the United Kingdom and Germany). The online survey was distributed by Qualtrics (Qualtrics XM, Utah, USA) in June 2020. A total of 3,298 responses were collected and were equally distributed across country and sex of respondent. Multinomial logistic regression was performed in SPSS Statistics (Version 26, IBM Corp, North Castle, New York, USA). More than 85% of respondents reported that they believe their dog is an ideal body weight. Results from logistic regression suggest that owners of younger dogs (0–2 years) are 5 times more likely to believe their dog is an ideal body weight compared to older dogs (over 11 years; P < 0.0001). Respondents who selected that they perform vigorous exercise, themselves, less often than 4 days per week were less likely to believe that their dog is an ideal body weight compared to those who reported vigorously exercising for more than 5 days per week (P < 0.05). Finally those who reported feeding their dog a fixed amount of food were more likely to believe their dog is an ideal body weight (P = 0.044) while those who reported restricting their dogs food intake to control weight were less likely to believe their dog is an ideal body weight (P < 0.0001). Overall, both human and dog dietary and exercise routines were predictive of a dog owner’s perception of their dogs body weight.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".