Milk Urea Testing as a Tool to Monitor Reproductive Performance in Ontario Dairy Herds
Bibliographic record
Abstract
Dairy herd improvement test-day data, including milk urea concentrations measured using infrared test method, were collected from 60 commercial Ontario Holstein dairy herds for a 13-mo period between December 1, 1995, and December 31, 1996. The objective of the study was to describe, at the cow and the group level, the relationship between DHI milk urea concentrations and reproductive performance in commercial dairy herds. When interpreted at the cow level, there was no association between milk urea and the risk for pregnancy from an insemination occurring within the 45-d period preceding test day. However, a negative curvilinear relationship existed between milk urea and the risk for pregnancy from a first, second, or third insemination event occurring within the 45-d period following test day, with the odds for pregnancy being highest when the milk urea on the test day preceding the insemination was either below 4.5 mmol/L or greater than 6.49 mmol/L, compared with a concentration between 4.5 and 6.49 mmol/L. When interpreted at the group level, there was no association between group mean milk urea for cows between 50 and 180 DIM, and the group conception rate for cows receiving a first, second, or third insemination event in the 45-d period either preceding or following test day. Thus, while DHI milk urea measurements may be useful as a management tool to improve the efficiency of production or reduce nitrogen excretion, through helping to optimize the efficiency of protein utilization, they may have limited utility as a monitoring or diagnostic tool for reproductive performance. The results of this study suggest that good fertility may be achieved across a broad range of milk urea concentrations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".