The Effect of Feeding a Dry Enzyme Mixture with Fibrolytic Activity on the Performance of Lactating Cows and Digestibility of a Diet for Sheep
Bibliographic record
Abstract
A dry enzyme mixture was added to the diets of lactating cows and growing lambs to evaluate its ability to improve milk production and nutrient digestibility, respectively. The enzyme mixture contained xylanase and cellulase activity over a broad range of pH (tested from 4 to 7). Twenty-four lactating cows between 50 and 150 d in milk and averaging about 40 kg of milk/ d were fed a total mixed ration (TMR) consisting of 26% [dry matter (DM) basis] corn silage, 17% alfalfa silage, 7% chopped alfalfa hay, and 50% concentrate. One-half of the cows were fed the TMR without supplementation and the remaining half of the cows were fed the same TMR supplemented with 10 g of the enzyme mixture/ cow per day. After 21 d, the treatments were crossed over for a second 21-d period. The dry enzyme mixture had no effect on DM intake, milk production, or milk composition. Addition of various concentrations of the enzyme mixture did not improve the in vitro digestion of neutral detergent fiber from the TMR. In a digestion trial, lambs were fed a commercial diet supplemented with 4 g of the enzyme mixture/lamb per day, and total feces and urine were collected. Although the ratio of enzyme to feed was much higher than it was in the experiment with lactating cows, addition of the enzyme mixture had no effect on the apparent digestion of DM, acid detergent fiber, neutral detergent fiber, or N in the diet.
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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 | 0.001 |
| 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 | 0.001 |
| 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".