Enzymes as Direct-Feed Additives for Ruminants
Bibliographic record
Abstract
Fibrolytic enzymes hold great potential to improve feed utilization and productivity in ruminants. In the past, it was believed that the endogenous activity against plant cell walls could not be augmented by supplementary exogenous enzymes. However, when diets of dairy and beef cattle are supplemented with commercial xylanases and cellulases, animal performance is significantly improved. The most likely site of action is the rumen rather than in the small intestine as is the case for poultry. Because of the complexity of the rumen environment, it has been difficult to identify the exact mode of action for this beneficial response. Since xylanases and cellulases are the main activities that occur in efficacious enzyme mixtures, it may be assumed that the enzymes are having a direct, additive effect on the hydrolysis of plant fiber in the rumen. However, evidence to date suggests that the benefits of exogenous enzymes is synergistic to ruminal endogenous enzymes. This synergy may explain why relatively small amounts of enzyme can have such large effects on animal productivity. Limitations to the exploitation of this technology are the development of an adequate screening system for new enzymes, and the identification of the specific enzyme activities that are critical for efficacy. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.014 | 0.011 |
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".