PSVI-16 Performance Response of Nursery Pigs Fed Increasing Levels of Fermentable and Structural Fiber
Bibliographic record
Abstract
Abstract Dietary fiber can provide functional benefits to nursery pigs, thereby improving gut health and growth performance. However, effects vary depending on the type and amount of fiber supplemented. The objective of this experiment was to determine the effects of dietary fermentable fiber (FRM), structural fiber (STR), and their interaction on growth performance of nursery pigs. In total, 3,014 weanling pigs (body weight = 5.71 ± 0.11 kg, 15 replicate pens/treatment, 21 – 23 pigs/pen) were allotted by pen in a randomized complete block design to treatments in a 3 × 3 factorial arrangement with 2 factors: FRM (12, 13, or 14%) and STR (3, 4, or 5%). Diets were formulated to maintain similar dietary protein and metabolizable energy, and rice hulls or beet pulp were added to achieve different levels of STR and FRM, respectively. Fiber concentrations were estimated using in vitro fermentation methods. Phase 1 and 2 diets were fed from day 0 – 11 and 11 – 21 post-weaning, respectively. A common diet was fed from d 21 – 43. Data were analyzed by general linear model in R with random effect of location block. Orthogonal contrasts tested for linear and quadratic effects of STR and FRM, and their interaction. Overall (d 0 – 43), increasing FRM increased average daily gain (ADG; linear, P < 0.05). As STR increased, overall ADG increased and then decreased (quadratic, P < 0.05), with 4% STR resulting in the greatest ADG. Interactions between fiber type were observed (P < 0.05) for final body weight, overall average daily feed intake, and overall gain:feed. Pigs fed 14% FRM and 5% STR had the numerically greatest final body weight and ADG. In summary, increasing STR up to 4% improved performance, and greater levels of FRM can further improve performance when higher levels of STR are provided in the diet.
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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) | 0.001 | 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".