The Potential of Novel Gene Editing-Based Approaches in Forages and Rumen Archaea for Reducing Livestock Methane Emissions
Bibliographic record
Abstract
Rising emissions of anthropogenic greenhouse gases such as carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) are a key driver of climate change, which is predicted to have myriad detrimental consequences in coming years if not kept in check. Given the potency of CH4 in terms of trapping heat in the atmosphere in the short term, as well as the fact that ruminant production currently contributes approximately 30% of anthropogenic emissions, there is an impetus to substantially decrease the generation of ruminant-derived CH4. While various strategies are being assessed in this context, a multi-faceted approach is likely required to achieve significant reductions. Feed supplementation is one strategy that has shown promise in this field by attenuating methanogenesis in rumen archaea; however, this can be costly and sometimes impractical. In this review, we examine and discuss the prospect of directly modulating forages and/or rumen archaea themselves in a manner that would reduce methanogenesis using CRISPR/Cas-mediated gene editing platforms. Such an approach could provide a valuable alternative to supplementation and has the potential to contribute to the sustainability of agriculture, as well as the mitigation of climate change, in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".