PSVI-30 Requirements of the sulfur amino acids in growing Labrador Retrievers using the indicator amino acid oxidation technique
Bibliographic record
Abstract
Abstract Over time, the need to update amino acid (AA) requirements for canines becomes increasingly important to ensure animals are healthy and free of nutritional disease. Each of the AA plays a crucial role in the metabolism and health of mammals. Of the essential AA, the total sulfur amino acids (TSAA), namely methionine (Met) and cysteine (Cys), are involved in metabolic functions such as protein metabolism, intestinal health, and urinary function. Additionally, Met can function as a precursor in the requirement of Cys. The purpose of this experiment was to use the indicator amino acid oxidation technique to determine amino acid requirements on growing Labrador Retrievers. A total of 12 dogs were subjected to 12 diets with varying levels of Met and Cys, ranging from deficient to excess. Diets were formulated to 1.6x NRC values for all indispensable amino acids. The control diet was fed for two days, followed by a day in which the test diet was fed, a tracer amino acid was supplied, and breath samples were collected. On test day, a priming dose of L-[1-13C]phenylalanine (Cambridge Isotope Laboratories, Inc.) based on the subject’s body weight was first supplied, followed by [1-13C]Phe doses every thirty minutes, spanning a four hour period. A respiration mask was placed on each subject every thirty minutes (Oxymax, Columbus Instruments), 13CO2 was collected, and enrichment was determined by isotope ratio mass spectrometry (IRMS). Results for IRMS were converted to atom percent excess (APE) and analyzed using a broken line model (JMP® Pro 15). Results showed that the Met and TSAA mean and population requirements were 0.78 ± 0.16 and 1.53 ± 0.21 g/1000kcal (mean ± 2SD), respectively. Knowledge gained from these studies is necessary as the petfood industry aims to deliver accurate diet formulations to the expansive canine population.
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 | 0.000 |
| 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".