Effects of a commercial blend of essential oils and monensin in a high-grain diet containing wheat distillers’ grains on in vitro fermentation
Bibliographic record
Abstract
Li, Y. L., Li, C., Beauchemin, K. A. and Yang, W. Z. 2013. Effects of a commercial blend of essential oils and monensin in a high-grain diet containing wheat distillers’ grains on in vitro fermentation. Can. J. Anim. Sci. 93: 387–398. Our objective was to evaluate in vitro effects of a commercial blend of essential oils (BEO) versus monensin (MON) on fermentation of a high-grain diet containing wheat distillers’ dried grains with solubles (DDGS). Two experiments were conducted. The first experiment was designed as a short-term batch culture to determine the optimum dose of BEO (0, 45, 90 or 180 mg kg−1DM) on fermentation variables. The second experiment was a completely randomized block design with a 2×2 factorial arrangement of treatments using the rumen simulation technique. A high-grain dietary substrate containing wheat DDGS was supplemented with 0 or 28 mg MON kg−1DM combined with 0 and 90 mg BEO kg−1DM. Monensin did not affect the total volatile fatty acid (VFA) concentration or the molar proportions of individual VFA, but reduced CH4production (mL L−1gas; P=0.01) and disappearance of DM (P=0.04). Supplementation of BEO did not affect VFA concentration, but decreased (P=0.01) the molar proportion of acetate and reduced (P=0.02) the acetate to propionate concentration ratio and CH4production (mL g−1digested organic matter). The BEO increased (P=0.01) disappearance of neutral detergent fiber without affecting disappearance of other nutrients. These results indicate that supplementation of high-grain dietary substrate containing wheat DDGS with BEO improved fermentation pattern by increasing propionate concentration, reducing CH4production, and increasing fiber digestibility. This commercial BEO could be a potential substitute for MON for beef cattle fed high-grain diets containing DDGS.
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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 | 0.001 | 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".