178 Programing the rumen microbiome to optimize microbial efficiency in high forage diets
Bibliographic record
Abstract
Abstract As the majority of energy and protein supplied to cattle arises as a result of ruminal fermentation, the rumen microbiome has an integral role in determining host feed efficiency. Counterintuitively, current evidence suggests that a less diverse rumen microbiome is associated with improved feed efficiency, possibly as a result of greater metabolic precision and avoidance of energy spilling fermentative pathways. The composition of the rumen microbiome is mainly determined by diet, but host traits such as rumen volume, rate of passage, rumination and immunity also have influence. Although less microbial diversity may improve feed efficiency in cattle fed a specific diet, reduced diversity may impair the ability of cattle to adapt to frequent changes in diet and the environment. Hydrogen exchange and capture is the energetic foundation of the rumen microbiome and considerable capital has been invested to develop additives that redirect hydrogen flow away from the reduction of CO2 to CH4 towards alternative sinks. These additives have been shown to reduce enteric CH4 emissions by 30 to 80%, but improvements in feed efficiency have been less than stoichiometric predictions. Approaches to improve the feed efficiency of cattle need to be multifaceted with consideration for host genetics, functional efficiency of the rumen microbiome, and the structure and composition of feed. Likewise, reductions in carbon emissions need to be broader than just CH4, with an appreciation of the role that cattle have within a circular bioeconomy to promote upcycling of nutrients and reductions in emissions from farming systems. Strategies to improve the efficiency of cattle production are a prerequisite for the sustainable intensification needed to ensure that the social license for milk and meat production from cattle is retained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".