PSIV-22 Effect of dietary glycine on growth performance and skin collagen abundance of nursery pigs fed low crude protein diets
Bibliographic record
Abstract
Abstract Ninety-six barrows (initial BW: 6.41 ± 0.61kg) were used to determine the effect of low crude protein (CP) diets supplemented with glycine and serine (G+S) on growth performance and skin collagen abundance. Barrows were randomly assigned to 24 pens and fed 1 of 3 dietary treatments ad-libitum in a 3-phase-feeding program: 1) corn-soybean meal diet (CON; 23.6– 20.5% CP); 2) low-CP diet (19.7–14.8% CP) supplemented with G+S to achieve the same concentration of G+S as CON; 3) similar to diet 2 but supplemented with glutamate instead of G+S to maintain the same CP concentration (GLU); diets were fed for 6 weeks. Individual BW and pen feed disappearance were measured weekly. On d 35, 1 pig/pen was sacrificed for determination of body composition and N retention, and skin samples were collected for collagen analysis. Final BW and overall ADG were greater for pigs fed CON versus GLU (P < 0.05) while G+S were intermediate; feed efficiency was not influenced by diet. Carcass weights on day 35 were greater for pigs fed CON (22.4kg) versus G+S (19.0kg) or GLU (20.4 kg; P < 0.05). Viscera weights on day 35 were greater for CON (3373g) versus G+S (2912g; P < 0.05); GLU were intermediate (3186g). Overall, whole-body N retention and N intake were greater for CON (11.98, 38.3 g/d for N retention and N intake, respectively) than G+S (9.02, 27.5 g/d) and GLU (9.52, 29.1g/d; P < 0.05). On day 35, pigs fed G+S and CON had greater skin collagen abundance (72.8%,and 72.0% for G+S and CON, respectively) versus GLU (67.2%;P < 0.05). Supplementing low-CP diets with G+S maintained BW and overall ADG (versus CON), but both G+S and GLU had reduced N retention; only G+S had skin collagen abundance not different from CON. Supplementing specific non-essential amino acids as well as measures beyond growth performance should be considered when formulating low-CP 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) | 0.000 | 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".