Strategies to Mitigate Enteric Methane Emissions in Ruminants: A Review
Bibliographic record
Abstract
Methane is the main greenhouse gas (GHG) emitted by ruminants. Mitigation strategies are required to alleviate this negative environmental impact while maintaining productivity and ruminants’ health. To date, numerous methane mitigation strategies have been investigated, reported and suggested by scientists to the livestock industry. In this review, the authors will focus on the commonly practiced and available techniques expanding the knowledge of the reader on the advances of methane mitigation strategies with a focus on the recent literature. Furthermore, the authors will attempt to discuss the drawbacks of the strategies in terms of animal health and performance reduction as well as the concept of feed and energy loss, adding an economic perspective to methane emission mitigation which is in the farmers’ direct interest. As a whole, many factors are effective in reducing undesired methane production, but this is definitely a complex challenge. Conclusively, further research is required to offer effective and efficient methane production mitigation solutions in ruminants worldwide, thus positively contributing to climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".