Application of Molecular Techniques to Better Understand the Roles of Rumen Microbiota in Cattle Feed Efficiency
Bibliographic record
Abstract
Feed efficiency, simply expressed as less feed inputs versus animal production outputs, can be measured in several ways, such as feed conversion ratio (FCR) and residual feed intake (RFI). FCR is a common measurement in beef cattle operations, and is the ratio of feed intake to live-weight gain. RFI is defined as the difference between actual and predicted feed intake after taking into account variability in maintenance and growth requirements. Rumen microbiota, which inludes bacteria, archaea, protozoa, and fungi, play an essential role in the digestion of lignocellulosic plant biomass, and can provide more than 70% of the host ruminants energy requirements via the production of volatile fatty acids (VFAs). Methane, a potent greenhouse gas (GHG), is produced in large quantities by the rumen microbiota, and is a known contributor to the global increase in GHG emissions. Studies have shown a negative relationship between methane emission and feed efficiency. Therefore, there is a need to study the feed efficiency from a rumen microbiome perspective and explore the probability of improving feed efficiency and hence reduce methane production in cattle by manipulating the rumen microbiome. The development of high-throughput sequencing technologies incuding metagenomics and metatranscriptomic analyses in the past decade has led to a sharp increase in understanding the rumen microbiota and associated function. As such, this mini-review will focus on the new findings during the last decade in cattle feed efficiency and the rumen microbiome.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".