236 Determination of amino acid requirements of lysine and tryptophan of >14 wk old growing Labrador retriever puppies using the indicator amino acid oxidation technique
Bibliographic record
Abstract
Abstract As the pet food industry develops products aimed to supply the nutrients necessary for the wellness and longevity of canines, it is important to understand the amino acid requirements needed for such health and growth. In this experiment, a total of six growing Labrador retriever puppies were studied during each testing period in order to determine their individual amino acid requirements through the indicator amino acid oxidation (IAAO) technique. To do this, the puppies were supplied with constant dietary Phe in the control and test diets. Excluding the control diet, a total of six diets with varying levels of the amino acid of interest were utilized in this experiment. 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 of best fit (JMP Pro 14.1). Through the segmented line regression of 13Phe oxidation, it was shown that lysine and tryptophan mean requirements were 0.627 and 0.204%, respectively. A 95% confidence interval was calculated as 0.627 ± 0.180 and 0.204 ± 0.182% (mean ± 2SD) for lysine and tryptophan, respectively, representing the population’s safe requirements. The determination of the amino acid requirements found in this study proves useful as the industry develops nutrient profiles specific to the needs of varying age groups and growing Labradors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".