Effect of creep and post-weaning feeding composition on piglets’ intestinal health and post-weaning growth according to their creep feed consumption status
Bibliographic record
Abstract
This study examined the impact of adding medium-chain fatty acids (MCFAs), yeast, and naked oats (NO) to creep and post-weaning diets on piglet growth before weaning, as well as their intestinal health and growth performance after weaning. Three diet type were evaluated: corn (C), NO, and NO combined with yeast and MCFAs (NO+). Piglets were classified as eaters or non-eaters of creep feeding, weaned at 20 days, and euthanized 9 days post-weaning for intestinal tissue analysis. The dietary treatments had minimal effect on the sows, except increased weight loss in the NO+ treatment ( P = 0.01). No differences were observed in litter growth or creep feed intake, or piglet growth after weaning across diets. In non-eater creep feeding, crypt depth was greater in the NO+ group compared to other treatments, while the opposite was true for eater creep feeding piglets (Interaction, P = 0.03). Additionally, villi height/crypt depth ratio was higher in NO+ than for C for eater creep feeding piglet, but lower in non-eaters compared to NO group (Interaction, P = 0.04). In conclusion, while NO, MCFAs, and yeast had a limited impact on growth, creep feed consumption could help reduce the negative impact of weaning on intestinal morphology.
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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".