Effect of xylanase and live yeast supplementation on growth performance, nutrient digestibility, and gut microbiome diversity of pigs
Bibliographic record
Abstract
Effect of xylanase (Xyl) and live yeast (LY) supplementation on gut microbiome composition, growth performance, and nutrient digestibility of weanling pigs was determined. A total of 180 weanling pigs were assigned to five treatments from weaning to market. Treatments were designated based on whether Xyl, LY, or their combination were fed in the first 2 wk postweaning or thereafter until finishing at day 141 postweaning. Treatments were (days 1–15; days 15–141): control–control, control–Xyl, Xyl–Xyl, LY–Xyl, Xyl + LY–Xyl. Xylanase was added at 16 000 BXU·kg−1 and LY at 1 kg·t−1. Pigs fed with LY and LY + Xyl from days 0–15 had greater body weight and average daily gain at day 15 compared with control (P < 0.05). Glucose transporter 2 mRNA was higher in LY and LY + Xyl groups on day 15 compared with control (P < 0.05). Xylanase supplementation from week 2 postweaning increased apparent total tract nutrient digestibility of gross energy, nitrogen, and phosphorus on day 43. Live yeast with or without Xyl improved growth performance in the first 2 wk after weaning; Xyl + LY–Xyl and control–Xyl groups had improved overall feed efficiency. In conclusion, LY and Xyl supplementation improved performance of weanling pigs in the first 2 wk after weaning with no effects on long-term 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) | 0.001 | 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 | 0.000 | 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".