Impact of strain and dose of live yeast and yeast derivatives on in vitro ruminal fermentation of a high-grain diet at two pH levels
Bibliographic record
Abstract
The objective of this study was to determine the effects of live yeast (LY) or yeast derivatives (YD) on gas production (GP), dry matter (DM) disappearance (DMD), fermentation characteristics, and microbial profiles in batch culture. The study was a completely randomized design with a factorial arrangement: 2 media pH × 5 yeasts × 4 dosages. An additional treatment of monensin (Mon) was added as a positive control for each pH level. Media pH was low (5.8) and high (6.5); the yeasts were three LY (LY1-3) and two YD (YD4-5); and doses were 0, 4 × 106, 8 × 106, and 1.6 × 107 cfu mL−1 for LY and 0, 15, 30, and 60 mg bottle−1 for YD. Substrate consisted of 10% silage and 90% concentrate (DM basis) and samples were incubated for 24 h. Media pH of 6.5 vs. 5.8, increased (P < 0.01) GP, DMD, and volatile fatty acid (VFA) concentrations but decreased (P < 0.01) NH3-N concentration and copy numbers of Ruminococcus albus, Ruminococcus flavefaciens, and Selenomonas ruminantium. Increasing dose of LY2 linearly (P < 0.05) increased DMD. Total VFA concentration was greater with LY2 (P < 0.01) than LY3 and YD5 at pH 6.5. Overall, adding yeast products improved (P < 0.05) DMD at pH 5.8, and increased VFA concentration compared with Mon. These results indicate that in vitro GP and DMD of a high-grain diet varied with source and dose of yeast supplementation. Some yeast products have the potential to improve fermentation of feedlot diets when supplemented at appropriate doses.
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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 | 0.001 |
| 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 | 0.001 | 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".