PSXIII-5 Nitrogen Retention and Protein Quality in Dogs and Cats Fed Commercial Pet Food.
Bibliographic record
Abstract
Protein, as a nitrogen-containing compound, is essential for growth and metabolism. A portion of the crude protein listed on pet foods may actually be from non-digestible organic nitrogen or potentially toxic inorganic non-protein nitrogen sources, neither of which are retained nor used by the animal. To analyze nitrogen retention and screen for non-protein nitrogen, four commercial pet foods for each dogs and cats and one lab-made diet for both species were evaluated and coated with a non-digestible marker, chromium oxide. Seven dogs and eight cats were randomly assigned each diet (n=4 for each diet). The dogs and cats were fed the chromium coated diets for 48 hours and urine was collected over this time, followed by total marked fecal collection on the subsequent days and plasma collection at the end of the feeding trial. Nitrogen retention was calculated based on nitrogen (%) consumed in feed verses nitrogen lost in feces and urine. Nitrite and nitrate concentrations in all samples was determined using a commercial assay kit. The amount of nitrogen retained ranged from 93–96% in dogs and in cats, nitrogen retention ranged from 91–95% but did not statistically differ among commercial diets. There were significant differences in the nitrate and nitrite concentrations in plasma, urine, and feces in both species. In the dogs, the concentrations of nitrite in plasma was significantly lower in commercial high protein diets than in low protein diets, with most nitrite and nitrate being excreted in the feces. In cats, nitrite and nitrate concentrations were highest in urine samples with little relation to dietary protein, suggesting that cats may have different handling of these compounds than dogs. Ultimately, the results of this study show that protein quality as assessed by utilization had no correlation to price and all diets lacked excess levels of nitrate or nitrite.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".