Redirecting rumen fermentation to reduce methanogenesis
Bibliographic record
Abstract
Methane production in ruminants has received global attention in relation to its contribution to the greenhouse gas effect and global warming. In the last two decades, research programs in Europe, Oceania and North America have explored a variety of approaches to redirecting reducing equivalents towards other reductive substrates as a means of decreasing methane production in ruminants. Some approaches such as vaccination, biocontrols (bacteriophage, bacteriocins) and chemical inhibitors directly target methanogens. Other approaches, such as defaunation, diet manipulations including various plant extracts or organic acids, and promotion of acetogenic populations, seek to lower the supply of metabolic hydrogen to methanogens. The microbial ecology of the rumen ecosystem is exceedingly complex and the ability of this system to efficiently convert complex carbohydrates to fermentable sugars is in part due to the effective disposal of H2 through reduction of CO2 to methane by methanogens. Although methane production can be inhibited for short periods, the ecology of the system is such that it frequently reverts back to initial levels of methane production though a variety of adaptive mechanisms. Hydrogen flow in the rumen can be modelled stoichiometrically, but accounting for H2 by direct measurement of reduced substrates often does not concur with the predictions of stoichiometric models. Clearly, substantial gaps remain in our knowledge of the intricacies of hydrogen flow within the ruminal ecosystem. Further characterisation of the fundamental microbial biochemistry of hydrogen generation and methane production in the rumen may provide insight for development of effective strategies for reducing methane emissions from ruminants.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".