Food Preferences in Dogs: Effect of Dietary Composition and Intrinsic Variables on Diet Selection
Why this work is in the frame
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Bibliographic record
Abstract
A ten-year food preference database (2007–2017) was used to relate food selection in dogs to the nutritional components of diets by doing a principal component analysis (PCA) and a linear regression between components obtained and dogs’ preferences. Intake and preference of preferred diets were analyzed by dogs’ sex, breed, age, body weight, and the season of the year (hot or cold). The fourth component after PCA presented a relation with food preferences (OR = −2.699, p = 0.026), showing negative correlations with crude fiber (rho = −0.196; P = 0.038) and dry matter (rho = −0.184; p = 0.049). Weight (OR = −1.35; p < 0.001), breed, both Boxer (OR = 10.62; p = 0.003) and Labrador Retriever (OR = 26.30; p < 0.001), and season (hot season) (OR = −5.27; p < 0.001) all influenced animals’ intake. Boxers presented a lower food preference compared to the other breeds (OR = −44.3; p < 0.001), while animals’ weight influenced preferences only in Boxers (OR = 2.02; p < 0.001). Finally, age and sex did not affect dogs’ preference or intake of preferred diets. Thus dry matter and fiber content have a negative impact on dogs’ food choices. Dogs’ weight, breed, and season affected food intake, but only breed affected dogs’ preferences, which is probably explained by adaptive changes in the detection, metabolization, and learning of nutritive food cues.
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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 it