Assessment of the Microbial Ecology of Ruminal Methanogens in Cattle with Different Feed Efficiencies
Bibliographic record
Abstract
Cattle with high feed efficiencies (designated "efficient") produce less methane gas than those with low feed efficiencies (designated "inefficient"); however, the role of the methane producers in such difference is unknown. This study investigated whether the structures and populations of methanogens in the rumen were associated with differences in cattle feed efficiencies by using culture-independent methods. Two 16S rRNA libraries were constructed using approximately 800-bp amplicons generated from pooled total DNA isolated from efficient (n = 29) and inefficient (n = 29) animals. Sequence analysis of up to 490 randomly selected clones from each library showed that the methanogenic composition was variable: less species variation (22 operational taxonomic units [OTUs]) was detected in the rumens of efficient animals, compared to 27 OTUs in inefficient animals. The methanogenic communities in inefficient animals were more diverse than those in efficient ones, as revealed by the diversity indices of 0.84 and 0.42, respectively. Differences at the strain and genotype levels were also observed and found to be associated with feed efficiency in the host. No difference was detected in the total population of methanogens, but the prevalences of Methanosphaera stadtmanae and Methanobrevibacter sp. strain AbM4 were 1.92 (P < 0.05) and 2.26 (P < 0.05) times higher in inefficient animals, while Methanobrevibacter sp. strain AbM4 was reported for the first time to occur in the bovine rumen. Our data indicate that the methanogenic ecology at the species, strain, and/or genotype level in the rumen may play important roles in contributing to the difference in methane gas production between cattle with different feed efficiencies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".