Relationship between rumen methanogens and methane production in dairy cows fed diets supplemented with a feed enzyme additive
Bibliographic record
Abstract
AIMS: To investigate the relationship between ruminal methanogen community and host enteric methane (CH(4) ) production in lactating dairy cows fed diets supplemented with an exogenous fibrolytic enzyme additive. METHODS AND RESULTS: Ecology of ruminal methanogens from dairy cows fed with or without exogenous fibrolytic enzymes was examined using PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analyses and quantitative real-time PCR (qRT-PCR). The density of methanogens was not affected by the enzyme additive or sampling times, and no relationship was observed between the total methanogen population and CH(4) yield (as g per head per day or g kg(-1) DMI). The PCR-DGGE profiles consisted of 26 distinctive bands, with two bands similar to Methanogenic archaeon CH1270 negatively correlated, and one band similar to Methanobrevibacter gottschalkii strain HO positively correlated, with CH(4) yield. Three bands similar to Methanogenic archaeon CH1270 or Methanobrevibacter smithii ATCC 35061 appeared after enzyme was added. CONCLUSIONS: Supplementing a dairy cow diet with an exogenous fibrolytic enzyme additive increased CH(4) yield and altered the composition of the rumen methanogen community, but not the overall density of methanogens. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first study to identify the correlation between methanogen ecology and host CH(4) yield from lactating dairy cows.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".