ORIGINAL ARTICLE: High calcium intake differentially inhibits nutrient and energy digestibility in two different breeds of growing dogs
Bibliographic record
Abstract
The current study was part of a larger investigation of two breeds of growing dogs (Dobenecker, 2002). The apparent digestibility of protein, fat, nitrogen-free extract (N-free extract) and organic matter as well as energy of a tripe and rice-based diet supplemented either with normal calcium [~1.1% dry matter (DM), normal calcium (NC)] or excess calcium [~3.6% DM, high calcium (HC)] was determined in two breeds of growing dogs of different sizes, including 30 Beagles and 44 Foxhound-Boxer-Ingelheim Labrador crossbred dogs (FBIs), at the ages of 12, 18 and 24 weeks. Apparent energy digestibility was significantly impaired by excess of calcium in both dog breeds, and the effect was stronger in FBIs than in Beagles (NC vs. HC in FBIs: 88.3 ± 2.6% vs. 84.7 ± 3.7%; NC vs. HC in Beagles: 89.0 ± 2.4% vs. 86.6 ± 3.4%; p < 0.05 in both FBIs and Beagles). The same was true for organic matter, N-free extract, crude protein and fat. The decrease in protein and fat digestibility was significant in FBIs, but not in Beagles. By contrast, the apparent digestibility of ash was lower in FBIs than in Beagles. Taken together, the results of the current study suggest that excess dietary calcium may be associated with systematic differences in nutrient digestibility by different breeds of dogs.
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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 | 0.000 |
| 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) | 0.001 | 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".