Impact of ferulic acid esterase‐producing lactobacilli and fibrolytic enzymes on ensiling and digestion kinetics of mixed small‐grain silage
Bibliographic record
Abstract
Abstract This study evaluated the effects of a ferulic acid esterase (FAE) and a non‐FAE‐producing inoculant applied alone or in combination with exogenous fibrolytic enzymes (EFE) on the fermentation and nutritive value of mixed grain (barley, oats and spring triticale) silage. The mixed crop was ensiled in laboratory mini‐silos either untreated (CON), or treated with a FAE inoculant (FAE), a non‐FAE inoculant (NFAE) or NFAE + EFE. Inoculated silages were lower (P < 0·01) in water‐soluble carbohydrate, whereas NFAE and NFAE + EFE silages had higher (P < 0·001) DM loss than other silages. FAE and NFAE silage had higher neutral detergent fibre (NDF), but were lower in NFAE + EFE than other silages (P < 0·001). Copy numbers of 16S rRNA associated with Lactobacillus buchneri were higher (P < 0·001) in NFAE and NFAE + EFE silages than in others, resulting in higher (P < 0·001) acetic acid in these silages. NFAE + EFE silage had lower (P < 0·001) in vitro gas production and NDF digestibility (NDFD) than other silages. FAE silage had higher (P < 0·01) in situNDFD than CON and NFAE + EFE silages. Inoculation of mixed small‐grain silage with NFAE‐producing inoculants combining EFE reduced NDFD.
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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) | 0.001 | 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 | 0.000 | 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".