PSIV-B-29 Late-Breaking: Evaluation of arginine requirements of growing > 14 week to 9 month old Labrador retrievers using differing diet formulations and the indicator amino acid oxidation technique
Bibliographic record
Abstract
Abstract Over time, the need to update amino acid requirements for canines is increasingly important due to genetic selection and the demand for more advanced diets. Amino acid requirements can be determined through differing methods including, but not limited to, nitrogen balance studies and the indicator amino acid oxidation (IAAO) technique. In this study, the IAAO method was studied on a total of six growing Labrador Retrievers to determine their individual amino acid requirements. Twelve test diets with varying levels of Arg were utilized to conduct this experiment. Six diets contained excess Lys with respect to Arg (Group 1), while the remaining diets contained lower Lys inclusions (Group 2). Diets were formulated to 1.6x NRC values for all indispensable amino acids, including Lys. Group 2 diet formulations were formulated the same as Group I, except the test Lys was set at 0.1% above test Arg levels. 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 piecewise model of best fit (JMP Pro 14.1). Through the segmented line regression, the arginine mean requirement and population safe requirements of growing dogs in Groups 1 and 2 was found to be 1.49 ± 0.30 and 1.38 ± 0.21 g/1000 kcal ME (mean ± 2SD), respectively.
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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) | 0.001 | 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".