Could propionate formation be used to reduce enteric methane emission in ruminants?
Bibliographic record
Abstract
To meet the increasing demand for meat and milk, the livestock industry has to increase its production. Without improving its efficiency, increased livestock, especially ruminant animals, will worsen the environmental damage, mainly from enteric CH4 emission. Enteric CH4 emission from ruminants not only exacerbates the global greenhouse effect but also reduces feed energy efficiency for the animals. The rumen disposes of metabolic hydrogen ([H]) primarily through methanogenesis and propionate formation. Theoretically, redirecting [H] from methanogenesis to propionate formation to reduce CH4 production could be a promising method for reducing greenhouse gas emission from ruminants, and may also increase animal productivity. However, the feasibility of such a shifting has never been synthetically discussed. Thus, the objectives of this review are to provide a brief overview of the biochemical pathways for disposal of H2 in the rumen, to analyze current feeding strategies that potentially promote propionate formation and their effects on methanogenesis, and to deliberate the challenge and opportunity associated with propionate formation as a sink to store the [H] shifting from enteric CH4 inhibition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".