Bibliographic record
Abstract
Domestic cats and dogs are carnivores that have evolved differentially in the nutrition and metabolism of amino acids. This article highlights both proteinogenic and nonproteinogenic amino acids. Dogs inadequately synthesize citrulline (the precursor of arginine) from glutamine, glutamate, and proline in the small intestine. Although most breeds of dogs have potential for adequately converting cysteine into taurine in the liver, a small proportion (1.3%-2.5%) of the Newfoundland dogs fed commercially available balanced diets exhibit a deficiency of taurine possibly due to gene mutations. Certain breeds of dogs (e.g., golden retrievers) are more prone to taurine deficiency possibly due to lower hepatic activities of cysteine dioxygenase and cysteine sulfinate decarboxylase. De novo synthesis of arginine and taurine is very limited in cats. Thus, concentrations of both taurine and arginine in feline milk are the greatest among domestic mammals. Compared with dogs, cats have greater endogenous nitrogen losses and higher dietary requirements for many amino acids (e.g., arginine, taurine, cysteine, and tyrosine), and are less sensitive to amino acid imbalances and antagonisms. Throughout adulthood, cats and dogs may lose 34% and 21% of their lean body mass, respectively. Adequate intakes of high-quality protein (i.e., 32% and 40% animal protein in diets of aging dogs and cats, respectively; dry matter basis) are recommended to alleviate aging-associated reductions in the mass and function of skeletal muscles and bones. Pet-food grade animal-sourced foodstuffs are excellent sources of both proteinogenic amino acids and taurine for cats and dogs, and can help to optimize their growth, development, and health.
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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 | 0.001 | 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) | 0.001 | 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".