Effects of essential oils from African basil on fermentation of <i>Andropogon gayanus</i> grass in the Artificial Rumen (RUSITEC)
Bibliographic record
Abstract
Kouazounde, J. B., Gbenou, J. D., He, M., Jardim, T., Jin, L., Wang, Y., Beauchemin, K. A. and McAllister, T. A. 2015. Effects of essential oils from African basil on fermentation of Andropogon gayanus grass in the Artificial Rumen (RUSITEC). Can. J. Anim. Sci. 95: 425–431. Essential oils (EO) from African basil (Ocimum gratissimum) have shown the potential to modify rumen microbial fermentation and reduce ruminal methane production from grass forages in in vitro batch cultures. However, it is not known whether the effects of EO on rumen microbial fermentation attenuate over time. The objective of this study was to examine the effects of African basil EO at 0 (control), 100, 200 and 400 mg L−1 incubation medium on microbial fermentation and methane production in the Rumen Simulation Technique (RUSITEC) using Andropogon gayanus grass as a substrate. African basil EO quadratically affected (P<0.05) methane production gas production and the pH of fermenter liquid. Total volatile fatty acid (VFA) production was linearly decreased (P<0.05) by African basil EO along with a shift in VFA profile towards less propionate and more acetate and butyrate. African basil EO quadratically altered (P<0.05) apparent dry matter, neutral detergent fiber digestibility, 15N incorporation into total microbial protein and the total production of microbial protein. This study confirms that EO from African basil quadratically affected methane emissions arising from the ruminal fermentation of A. gayanus grass mainly by reducing overall digestibility of the forage.
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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".