Fibre digestion by rumen microbiota — a review of recent metagenomic and metatranscriptomic studies
Bibliographic record
Abstract
Plant biomass is the most abundant renewable resource on the planet, and the biopolymers of lignocellulose are the foundation of ruminant production systems. Optimizing the saccharification of lignocellulosic feeds is a crucial step in their bioconversion to ruminant protein. Plant cell walls are chemically heterogeneous structures that have evolved to provide structural support and protection to the plant. Ruminants are the most efficient digesters of lignocellulose due to a rich array of bacteria, archaea, fungi, and protozoa within the rumen and lower digestive tract. Metagenomic and metatranscriptomic studies have enhanced the current understanding of the composition, diversity, and function of the rumen microbiome. There is particular interest in identifying the carbohydrate-active enzymes responsible for the ruminal degradation of plant biomass. Understanding the roles of cellulosomes- and polysaccharide-utilising loci in ruminal fibre degradation could provide insight into strategies to enhance forage utilisation by ruminants. Despite advancements in “omics” technology, the majority of rumen microorganisms are still uncharacterised, and their ability to act synergistically is still not understood. By advancing our current knowledge of rumen fibre digestion, there may be opportunity to further improve the productive performance of ruminants fed forage diets.
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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 | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".