Impacts of hybrid rye and fiber level in the diet of pregnant and early lactation sows on the reproductive performance and fecal microbiota of sows and their piglets
Bibliographic record
Abstract
This study aimed to evaluate the effects of dietary fiber level and hybrid rye content on sow reproductive performance and microbiota as well as piglet growth and microbiota. A total of 245 sows were assigned to one of four diets from breeding until the first week of lactation: Control (10% neutral detergent fiber (NDF)); Fiber (20% NDF); Rye30 (30% rye, 20% NDF); Rye60 (60% rye, 20% NDF). Fecal samples were taken 7 days post-farrowing from sows and three piglets per litter. Reproductive performance was mostly unaffected ( P > 0.10), except for lower stillbirth rates in Fiber and Rye30 and reduced mortality from birth to 24 h after farrowing in Rye30 but higher in Fiber ( P < 0.05). Litter gain during lactation showed no difference between groups. Microbiota analysis revealed lower alpha diversity in Control piglets compared to other groups ( P < 0.05), but alpha diversity of sow microbiota was not affected by treatments. Sows and piglets in Fiber, Rye30, and Rye60 treatments showed higher Prevotellaceae abundance in fecal samples ( P < 0.05). In conclusion, Fiber or Rye30 treatments slightly affected reproductive performance. The Fiber, Rye30, and Rye60 treatments influenced sow microbiota and enhanced establishment in piglets, highlighting the potential of dietary fiber and hybrid rye for microbiota modulation.
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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 | 0.000 |
| 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".