Screening of live yeast and yeast derivatives for their impact of strain and dose on <i>in vitro</i> ruminal fermentation and microbial profiles with varying media pH levels in high‐forage beef cattle diet
Bibliographic record
Abstract
BACKGROUND: Yeast products showed beneficial effects with respect to stabilizing ruminal pH, stimulating ruminal fermentation and improving production efficiency. Batch cultures were conducted to evaluate the effects of yeast products on gas production (GP), dry matter disappearance (DMD) and fermentation characteristics of high-forage substrate. The study was a two media pH (5.8 and 6.5) × five yeasts (three live yeasts, LY: LY1, LY2, LY3; two yeast derivatives, YD: YD4, YD5) × four dosages factorial arrangement, with monensin (Mon) assigned as a positive control. RESULTS: -N concentration differed (P < 0.05) among yeasts but varied with media pH or yeast dosages. Increasing doses of LY3 linearly increased DMD (P < 0.04) and VFA concentration (P < 0.001) at media pH 5.8. The DMD linearly (P < 0.02) increased with increased addition of YD4 (pH 6.5) and YD5 (pH 5.8) and the ratio of A:P linearly decreased (P < 0.01) with the addition of YD4 or YD5 at pH 5.8. Overall greater (P < 0.05) GP, A:P (pH 5.8) and DMD (pH 6.5) were observed with yeast products than with Mon. CONCLUSION: LY3 appeared to be an interesting candidate for improving rumen digestibility and fermentation efficiency, particularly at low media pH. YD4 or YD5 improved fermentation efficiency and can be potentially fed as an alternative to Mon. © 2019 Her Majesty the Queen in Right of Canada Journal of the Science of Food and Agriculture © 2019 Society of Chemical Industry.
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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".