PSIII-14 Early Introduction of Structural Fiber Improves Growth Performance and Gut Health of Nursery Pigs
Bibliographic record
Abstract
Abstract Structural fiber inclusion in post-weaning piglet feeds has been previously demonstrated to improve growth performance and gut health. The objective of this study was to determine whether a pre-weaning inclusion of structural fiber (STR) would bring added value to piglet performance and gut health and if the potential benefits could be maintained at a later stage. A total of 26 litters, were creep fed either a control or high STR diet (HSTR; 4% oat hulls) for 10 d before weaning. Upon weaning, a total of 240 pigs (body weight: 6.7 ± 0.5 kg, 5 pigs/pen, 12 pens/treatment) of mixed sex, blocked by body weight, were randomly allocated to either a control or HSTR diet for 2 weeks. The experimental treatments were as follows, depending on pre- and post-weaning dietary intervention: control/control; control/HSTR; HSTR/control; and HSTR/HSTR. Oat hulls were added to expense of corn starch to produce the HSTR treatment diet. Diets were adjusted with wheat and soy oil to achieve equal amount crude protein and SID Lysine but had up to 40 kcal net energy difference. Pigs were fed a common diet for the rest of nursery phase. Data were analyzed by general linear model in R. Results indicated pig performance was not different at weaning. Addition of STR in the pre-weaning phase increased average daily gain in the last post-weaning phase (P < 0.05), regardless of the post-weaning diets (Table 1). When HSTR was fed both in the pre and early post-weaning phase, greater fermentation (indicated by numerically greater butyrate and caproate concentration in colon digesta) was observed at 21 d post-weaning. In conclusion, providing HSTR during pre-weaning phase improves performance during later nursery phase and if STR is used both pre- and post-weaning, gut fermentation is affected.
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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) | 0.003 | 0.001 |
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".