Energy intake, growth rate and body composition of young Labrador Retrievers and Miniature Schnauzers fed different dietary levels of vitamin A
Bibliographic record
Abstract
Research in rodents has shown that dietary vitamin A reduces body fat by enhancing fat mobilisation and energy utilisation; however, their effects in growing dogs remain unclear. In the present study, we evaluated the development of body weight and body composition and compared observed energy intake with predicted energy intake in forty-nine puppies from two breeds (twenty-four Labrador Retriever (LAB) and twenty-five Miniature Schnauzer (MS)). A total of four different diets with increasing vitamin A content between 5·24 and 104·80 μmol retinol (5000-100 000 IU vitamin A)/4184 kJ (1000 kcal) metabolisable energy were fed from the age of 8 weeks up to 52 (MS) and 78 weeks (LAB). The daily energy intake was recorded throughout the experimental period. The body condition score was evaluated weekly using a seven-category system, and food allowances were adjusted to maintain optimal body condition. Body composition was assessed at the age of 26 and 52 weeks for both breeds and at the age of 78 weeks for the LAB breed only using dual-energy X-ray absorptiometry. The growth curves of the dogs followed a breed-specific pattern. However, data on energy intake showed considerable variability between the two breeds as well as when compared with predicted energy intake. In conclusion, the data show that energy intakes of puppies particularly during early growth are highly variable; however, the growth pattern and body composition of the LAB and MS breeds are not affected by the intake of vitamin A at levels up to 104·80 μmol retinol (100 000 IU vitamin A)/4184 kJ (1000 kcal).
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".