249 Increasing Structural Fiber Improves Growth Performance of Nursery Pigs
Bibliographic record
Abstract
Abstract Fiber ingredients in swine diets have various components that affect the intestinal tract distinctively. The objective of this study was to determine the effect of structural fiber sources on growth performance of nursery pigs. A total of 1,760 weanling pigs (initial BW = 6.12 ± 0.3 kg, 16 reps/trt, 22 pigs/pen) were used in a study with 5 dietary treatments: 1) Complex nursery diet with no additional fiber (CON); 2) CON + 2% rice hulls; 3) CON + 4% rice hulls; 4) CON + 6% wheat middlings; 5) CON + 12% wheat middlings. Fiber ingredients were added at the expense of corn and some processed soybean meal to maintain similar dietary protein levels. Using an in vitro fermentation estimation of fermentation for rice hulls and wheat middlings, diets were constructed to add similar levels of structural (non-fermentable NDF) fiber between low and high additions of rice hulls and wheat middlings. Pens across two barns were randomly allotted independent of one another and within a location block to one of the five treatments on d 0 of a two-phase study (d 0–11 and d 11–20.5 post-weaning). Data were analyzed by general linear model in R. Mortality and removal data were analyzed as a generalized linear mixed model with a binomial distribution. Contrasts tested the effect of additional fiber (CON vs treatments 2–5), effect of medium vs high fiber (treatment 2/4 vs 3/5), effect of source of fiber (treatment 2/3 vs 4/5), and the interaction of level and source of fiber. Overall (Table 1), additional fiber resulted in increased ADFI (P < 0.05) and tended to increase ADG (P < 0.10). Probability of mortality and removal was reduced (P < 0.05) when additional fiber was included. In summary, increasing the level of structural fiber improved performance and livability of nursery pigs.
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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".