149 Effect of Dietary Structural Fiber and Fermentable Protein on Growth Performance in Post-Weaning Pigs
Bibliographic record
Abstract
Abstract Excessive protein fermentation in the hind gut is harmful to piglet gut health. Addition of fermentable fiber has been used as a mitigating strategy. However, the impact of structural fiber (STR) and its interactive effect with fermentable protein (FP) are less studied. In the present study, in a 2 x 2 factorial arrangement setting, 240 weaned pigs with initial body weight of 6.9 ± 1.1 kg (6 mixed sex pigs/pen, 10 pens/treatment) were allotted in a randomized complete block design to 1 of the following diets: low STR, low FP (control); low STR, high FP; high STR, low FP or high STR, high FP diet. A standard corn-soy based nursery diet was used as control. The treatment diets were supplemented with either oat hulls to increase STR or canola meal to increase FP level. Experimental diets were fed during the first 3 weeks post-weaning and followed by a common diet for the rest of nursery phase. Data was analyzed by general linear model in R. In summary, STR significantly increased average daily gain (ADG) during week 1 (P < 0.05) and in the entire nursery phase (P = 0.01) and tended to increase average daily feed intake (ADFI) during these periods (P < 0.10) (Table 1). Interaction effect tended to be different (P < 0.10) on ADG and ADFI during week 3 post-weaning, with pigs fed with high STR, high FP diet having higher ADG and ADFI numerically compared with the same level FP but lower STR treatment. An interaction effect was observed on gain:feed (G:F) for entire nursery phase (P = 0.05), where high STR, low FP diet resulted in numerically highest G:F. Conclusively, high STR improves performance during entire nursery phase and when dietary FP is high, increasing STR may alleviate the negative impact on 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) | 0.001 | 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 | 0.001 | 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".