PSXI-12 Determination of Protein Requirements for Labrador Retriever Pups (> 14 wk-9 Mo) Using the Indicator Amino Acid Oxidation (Iaao) Technique
Bibliographic record
Abstract
Abstract Providing protein to growing puppies in accurate amounts ensures that animals have enough energy and nutrients for depositing muscle, growing bones, and maintaining joint health. Inclusion amounts must be provided at correct doses, considering a diet with excess protein can lead to harmful outcomes such as hyperkalemia or acidosis. To determine the protein requirements in growing Labrador Retrievers, the indicator amino acid oxidation (IAAO) technique was utilized in six puppies (>14 wk-9 mo). Dogs were supplied with constant dietary Phe across diets. 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 body weight of the subject was supplied, followed by [1-13C]Phe doses every 30 minutes, spanning a four-hour period. A respiration mask was placed on each subject every 30 minutes (Oxymax, Columbus Instruments), 13CO2 was collected, and enrichment was determined by isotope ratio mass spectrometry (IRMS). IRMS results were converted to atom percent excess (APE) and analyzed using a segmented model of best fit (JMP Pro 16). The outcome of this experiment determines that if essential amino acids and non-essential amino acid nitrogen requirements are met, Labrador Retriever puppies will have mean and population safe requirements (mean±2SD) of 47.5 ± 1.325 g/1,000 kcal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".