Effects of essential oils from medicinal plants acclimated to Benin on <i>in vitro</i> ruminal fermentation of <i>Andropogon gayanus</i> grass
Bibliographic record
Abstract
BACKGROUND: Plants from West Africa commonly used in both human and veterinary medicine contain various secondary metabolites. However, their potential in mitigating ruminal methane production has not been explored. This study examined the effects of seven essential oils (EOs) from plants acclimated to Benin at four dosages (100, 200, 300 and 400 mg L(-1)), on in vitro rumen microbial fermentation and methane production using Andropogon gayanus grass as a substrate. RESULTS: Compared to control, Laurus nobilis (300-400 mg L(-1) ), Citrus aurantifolia (300-400 mg L(-1)) and Ocimum gratissimum (200-400 mg L(-1)) decreased (P < 0.05) methane production (mL g(-1) DM) by 8.1-11.8%, 11.9-17.8% and 7.9-30.6%, respectively. Relative to the control, reductions in methane (mL g(-1) DM) of 11.4%, 13.5% and 14.2% were only observed at 400 mg L(-1) for Eucalyptus citriodora, Ocimum basilicum and Cymbopogon citratus, respectively. These EOs lowered methane without reducing concentrations of total volatile fatty acids or causing a shift from acetate to propionate production. All EOs (except M. piperita) reduced (P < 0.05) apparent dry matter (DM) disappearance of A. gayanus. CONCLUSIONS: The current study demonstrated that EOs from plants grown in Benin inhibited in vitro methane production mainly through a reduction in apparent DM digestibility.
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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".