PSXI-31 Program Chair Poster Pick: Determination of the threonine requirement in adult and senior Labrador Retrievers using the indicator amino acid oxidation technique
Bibliographic record
Abstract
Abstract Although nutrient profiles for canines have been developed in the past, the need to update amino acid (AA) requirements has gained importance as genetic selection changes the recommended nutrients. Correctly feeding AA to canines can have enormous effects, considering a deficiency or excess of such nutrient can lead to weight loss, disease, or in some cases, death. Amino acid requirements can be determined through the nitrogen balance or indicator amino acid oxidation (IAAO) methods. In this experiment, the IAAO technique was used to determine the threonine (Thr) requirement in Labrador retrievers. A total of six dogs (6 adult and 6 senior) were subjected to six diets with varying levels of Thr, 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 AA 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 piecewise model of best fit (JMP® Pro 15). The segmented line regression showed that the Thr mean and population requirements were determined to be 1.21 ± 0.24 and 0.92 ± 0.17 g/1000kcal (mean ± 2SD) for adult and senior dogs, respectively. As the pet food industry becomes more specialized in diets relating to aging, and diseased canines, updating the amino acid requirements related to such animals is increasingly important.
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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) | 0.012 | 0.003 |
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".