Fibrolytic enzymes and a ferulic acid esterase‐producing bacterial additive applied to alfalfa hay at baling: effects on fibre digestibility, chemical composition and conservation characteristics
Bibliographic record
Abstract
Abstract This study evaluated the effect of two fibrolytic enzyme products, applied at baling, on the chemical composition and digestibility of alfalfa hay. Three replicate bales of alfalfa hay (82% dry matter) were produced with the application of one of five treatments including an untreated control and one of two fibrolytic enzyme products (DYC and ECO), either applied alone or in combination with a ferulic acid esterase‐producing bacterial additive. The enzyme products were applied on the basis of endoglucanase activity. The neutral detergent fibre (NDF) concentration and accumulated temperature after storage of hay produced using DYC‐ or ECO‐based treatments were greater (P < 0·05) than untreated hay, except for hay bales produced using ECO alone. Bales produced using ECO‐based treatments had a greater (P < 0·05) in vitro NDF digestibility compared with untreated bales. The application of fibrolytic enzymes at baling may potentially improve NDF digestibility without negatively affecting chemical composition or increasing aerobic deterioration. However, the effects of fibrolytic enzymes varied depending on the product applied. Combining ferulic acid esterase‐producing bacterial additives with fibrolytic enzymes did not improve the nutritive value of hay after storage.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".