PSIV-8 Effect of Feeding Levucell SB® 10 Titan Advantage and YANG on Performance, Blood Parameters, and Fecal VFA of Nursery Pigs
Bibliographic record
Abstract
Abstract A total of 496 weaned pigs (19 ± 1 d of age) were used in a 3-phase feeding program to evaluate effects of Levucell SB 10 Titan Advantage, a live yeast, Saccharomyces cerevisiae boulardii (LEV; Elanco) and a non-viable yeast product (YANG; Elanco) on performance, complete blood count (CBC), and fecal VFA of nursery pigs. Pigs were blocked by weaning BW and gender and allotted to 44 pens (11–12 pigs/pen) which was assigned to one of 4 dietary treatments (11 pens/treatment). The study was designed as 2 x 2 factorial with LEV and YANG. Pigs were fed to meet or exceed nutrient requirements (NRC, 2012). Blood (d 10 and 22) and fecal samples (d 22) were collected from one pig per pen for CBC and VFA analyses. During d 0–7, feeding LEV or YANG alone improved (P < 0.05) G:F by 20% and 12%, respectively. There was a tendency of LEV*YANG interaction (P < 0.10) where feeding LEV increased (P < 0.05) BW and ADG compared to the control (CON). During d 0–14, feeding YANG with LEV reduced (P < 0.05) BW, ADG, and G:F compared to feeding YANG or LEV alone. Pigs fed LEV had an improved G:F compared to pigs fed CON or YANG with LEV (P < 0.05). During d 21–42, there was a tendency (P < 0.10) for a LEV*YANG effect where pigs fed CON had a lower G:F compared to other treatments. Overall, feeding LEV alone increased (P < 0.01) G:F (2.4%) and numerically increased final BW (0.60 kg) compared to CON. LEV tended to increase blood lymphocytes (P = 0.11) and increased platelets and platelet hematocrit (P < 0.05) compared to CON. There were no differences in fecal VFA measures. The results of this study indicate that feeding LEV increased G:F 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) | 0.001 | 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) | 0.002 | 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".