257 Effect of the Essential Amino Acid-Nitrogen to Total Nitrogen Ratio on Lysine Requirement for Nitrogen Retention in Growing Pigs
Bibliographic record
Abstract
Abstract Low-protein (LP) diets may become limiting in nitrogen (N), affecting utilization of essential amino acids (EAA) for nitrogen retention (NR). The objective of the present study was to determine the effect of the EAA-N to total N ratio (EAA-N:TN) on lysine (Lys) requirement for NR. A total of 80 growing barrows (21.5±0.89 kg initial BW) were randomly assigned to 1 of 10 dietary treatments (n=8 pigs/treatment) in 8 blocks in a 2 × 5 factorial arrangement with factors of EAA-N:TN (low vs. high) and Lys content (0.82, 0.92, 1.02, 1.12, and 1.22% standardized ileal digestible [SID] Lys). Diets were formulated to meet EAA requirements with the low ratio diet (LR; 16.8% crude protein) having an ideal EAA-N:TN of 0.48 and the high ratio diet (HR; 15.5% crude protein) formulated to be limiting in NEAA with an EAA-N:TN of 0.55. Diets were fed at 2.8 × maintenance metabolizable energy requirements. After a 7-d dietary adaptation, a 4-d N-balance period was conducted where fresh fecal samples were collected daily and quantitative urine samples were collected over a 24-h period. Nitrogen-balance was determined as the difference between N intake minus fecal and urinary N output. Data were analyzed using a MIXED model with fixed effects of ratio, Lys, and their interactions, and block as a random effect. Lysine requirement based on two-phase linear or quadratic modeling was estimated using PROC NLIN. Nitrogen retention increased linearly with increasing Lys levels regardless of ratio (P< 0.01). Quadratic modelling estimated the Lys requirement to maximize NR at 17.83 g/d in pigs fed HR diets at 1.21% SID (R2 = 0.53; P< 0.05), but no breakpoint was achieved for pigs fed LR diets, which showed a linear response (R2 = 0.79; P< 0.05). These results suggest that NR is limited by NEAA or TN in HR diets.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".