Evaluating the effects of fibrolytic enzymes on rumen fermentation, omasal nutrient flow, and production performance in dairy cows during early lactation
Bibliographic record
Abstract
This study was performed to evaluate the effects of pre-treating a barley-silage-based diet with an exogenous fibrolytic enzyme derived from Trichoderma reesei (FETR, a mixture of xylanase and cellulase) on lactation performance, omasal nutrient flow and digestibility, rumen fermentation characteristics, and rumen pH profile in Holstein dairy cows during early lactation. The dairy trial was conducted using nine Holstein dairy cows (averaging 46 ± 24 days in milk and 697 ± 69 kg body weight, six cows were fitted with a rumen cannula, and three were non-cannulated). Two groups of cows were randomly assigned to each of the dietary treatments in a crossover design: control (without FETR supplementation) and supplemented [with 0.75 mL of FETR·kg−1 dry matter (DM) of the diet based on our previous study]. The application of FETR tended to decrease the DM intake compared with control. There were no effects of FETR (P > 0. 10) on omasal nutrient flow and digestibility, rumen fermentation characteristics, and rumen pH profile. In conclusion, this study lacks evidence that the fibrolytic enzyme (at a level of 0.75 mL of FETR·kg−1 DM) can affect nutrient digestibility, ruminal fermentation, and the performance of early-lactation cows. Further study with larger animal trials are needed.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".