Nutritional management for enteric methane abatement: a review
Bibliographic record
Abstract
A variety of nutritional management strategies that reduce enteric methane (CH4) production are discussed. Strategies such as increasing the level of grain in the diet, inclusion of lipids and supplementation with ionophores (>24 ppm) are most likely to be implemented by farmers because there is a high probability that they reduce CH4 emissions in addition to improving production efficiency. Improved pasture management, replacing grass silage with maize silage and using legumes hold some promise for CH4 mitigation but as yet their impact is not sufficiently documented. Several new strategies including dietary supplementation with saponins and tannins, selection of yeast cultures and use of fibre-digesting enzymes may mitigate CH4, but these still require extensive research. Most of the studies on reductions in CH4 from ruminants due to diet management are short-term and focussed only on changes in enteric emissions. Future research must examine long-term sustainability of reductions in CH4 production and impacts on the entire farm greenhouse gas budget.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".