Factors affecting the milk urea nitrogen concentration in Chinese Holstein cows
Bibliographic record
Abstract
Abstract In order to investigate the factors affecting milk urea nitrogen in Chinese Holstein cows, a large commercial dairy farm participated in a 30-month study. In this study, the mean milk urea nitrogen concentration was 11.75 mg/dl. The milk urea nitrogen reached its maximum value on day 90 of lactation for the first parity and the third or higher parities, but it peaked at the end of lactation for the second parity. The milk urea nitrogen of the first parity was lower than that of other parities. The milk urea nitrogen showed its minimum level in January, and reached its maximum in July. The milk urea nitrogen at the first month of lactation in cows calving in summer was higher than other seasons, while at the fourth month of lactation, the milk urea nitrogen of cows calving in autumn was significantly lower than in cows calving in other seasons. Positive correlations were observed between daily milk yield, net energy for lactation, crude protein and milk urea nitrogen for the first and third parities, but negative correlations were observed in the second parity. The milk urea nitrogen showed significantly positive correlations with fat content, total solid content and daily matter intake for all parities. A negative correlation was observed between milk urea nitrogen and protein content, with the exception of the second parity. For all data, as milk urea nitrogen concentration increased, milk protein content decreased. It has been recommended that milk urea nitrogen concentration should be evaluated in combination with parity, days in milk, season (or month), daily matter intake and dietary nutritional components, in order to improve the management and economic benefits of dairy farm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".