185 The effect of reduced dietary glycine and supplemental threonine on growth performance and collagen content in skin of pigs fed low crude protein diets.
Bibliographic record
Abstract
Thirty-five barrows (BW=15.1 ± 2.72 kg) were used to determine the effect of supplementing dietary Thr above estimated requirements on pig growth and skin collagen abundance when fed low-CP low-Gly diets. Pigs were randomly assigned to 1 of 5 dietary treatments: 1) Control, semi-purified diet that met EAA requirements (CON; 12.1% CP as-fed); the final proportion of NEAA was similar to that found in whole-body protein. The remaining diets were formulated by reducing Gly and Ser content to 60 and 20%, respectively, of CON and supplementing with either Glu or Thr at 2 levels each, to maintain similar CP concentrations: For diets 2 and 3, Thr was included at 1.59% (LT) or 2.34% (HT) at the expense of the NEAA mix. For diets 4 and 5, Glu was included at 3.47% (LG) or 4.64% (HG) at the expense of the NEAA mix. Pigs were fed at 2.8 × estimated ME requirements for maintenance in 3 equal meals per day over a 21-day experimental period. At slaughter, skin samples were collected for collagen analysis. All statistical analyses were determined using Proc GLIMMIX. There was no difference in initial or final BW. Overall, ADG for pigs fed diets supplemented with Glu (378g/d) was greater than those fed diets supplemented with Thr (359 ± 9.45g/d; P=0.041). Feed efficiency for pigs supplemented with Glu (0.431) was greater than diets supplemented with Thr (0.411 ± 0.01; P=0.074). Pigs fed diets supplemented with LG, HG and HT had reduced skin collagen content compared to CON (P<0.05), but collagen content was not different between pigs fed LT versus CON diets. Diets supplemented with Thr decreased performance by partitioning away from protein synthesis towards collagen production. Supplementing reduced-CP diets with 1.59% Thr can maintain normal skin collagen content. NEAA supply should be considered when formulating low CP diets beyond effects on growth performance.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".