Dietary phenylalanine requirement of adult Labrador Retrievers using the direct oxidation approach
Bibliographic record
Abstract
Historically, the determination of amino acid (AA) requirements in dogs have been done in growing dogs and have been invasive in nature, requiring long periods of confinement and exposure to diets deficient in AA, which result in a reduction in the health and well being of dogs during the study period. Therefore, we were interested in examining whether carbon oxidation methods, with short periods of exposure to diets deficient in a single AA, could be successfully applied to determine AA requirements in dogs. Four adult Labrador Retrievers consumed 7 levels of phenylalanine (Phe), and excess tyrosine (Tyr) in random order and in combination with kibble that was formulated to be deficient in Phe and adequate in dietary energy (3730 kcal/kg as fed) and fed at ~13.5 g kg −1 BW d −1 . Dogs were fed test diets for two days prior to conducting the tracer studies. The Phe requirement was determined by applying a 2‐phase linear regression crossover model to tracer oxidation (F 13 CO2). The mean Phe requirement was 3.27 g/kg diet (upper 95% CI= 4.96 g/kg diet) on a dry matter basis. The current NRC estimate of the adequate intake for Phe is 4.50 g/kg diet and based on growth and nitrogen balance in growing dogs. This magnitude of difference is similar to that seen when estimates of AA requirements in humans using nitrogen balance vs. carbon oxidation methods are compared.
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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) | 0.001 | 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".