Effects of fat coated rumen bypass lysine and methionine on performance of dairy cows fed a diet deficient in lysine and methionine
Bibliographic record
Abstract
ABSTRACT Two experiments were conducted to evaluate the effect of fat coated rumen bypass lysine (RPLys) and methionine (RPMet) on the lactation performance of dairy cows. In Experiment 1, three lactating cows were supplied with RPLys and fat coated DL‐Met which was highly protected (H‐RPMet) as an indigestible marker, and total fecal emission was collected for 72 h following administration. Measuring the proportional difference in fecal excretion of lysine derived from RPLys relative to methionine derived from H‐RPMet, the intestinal availability of RPLys was estimated to be 66.2%. In Experiment 2, 20 multiparous Holstein cows producing approximately 40 kg/day of milk were assigned to two treatments; fed RPLys (16 g/day as lysine) and RPMet (6.5 g/day as methionine) or none (control) from 5 to 21 weeks postpartum. The consumption of dry matter, organic matter, crude protein, neutral detergent fiber and acid detergent fiber were significantly more in the control cows throughout the experimental period. Their milk protein yield, the contents of their milk protein and milk fat were higher by 0.03 kg (P = 0.03), 0.06% (P < 0.001) and 0.11% (P = 0.07), respectively, in the treatment group compared to the control. These results suggest that the RPLys and RPMet used in this study improved the lactation performance of dairy cows.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".