Assessment of the potential of feed enzyme additives to enhance utilization of corn silage fibre by ruminants
Bibliographic record
Abstract
We hypothesized that the fermentation of corn silage by a mixed culture of rumen microorganisms in an in vitro system could be increased using exogenous fibrolytic enzyme additives (FE), and that the improvement would depend on the dose of cellulase or xylanase activity provided. An in vitro assay was used to determine the effects of FE on gas production (GP) and degradability of fibre after 24 h of incubation in buffered ruminal fluid. Eight FE with endoglucanase and xylanase activities were evaluated at one dose (0.5 mg g-1 of forage dry matter), providing variable units of enzymic activity. Only one product improved fibre degradability (9.1 and 29.9% increases for neutral and acid detergent fibre, respectively; P < 0.05). The FE were reassessed when added to supply the same dose of enzymic activity: 807 units of endoglucanase or 477 units of xylanase activity g-1 of forage dry matter (a unit was defined as nmol of reducing sugar released min-1). The FE had greater impact on GP (21% increase; P < 0.05) and fibre degradation (29 and 60% increases for neutral and acid detergent fibre, respectively; P< 0.05) when equalized for endoglucanase activity than when equalized for xylanase activity. Enzyme products high in endoglucanase activity and low in xylanase activity have the potential to improve the use of corn silage by ruminants. Key words: Corn silage, degradability, exogenous fibrolytic enzymes, gas production
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".