Factors Associated with Milk Urea Concentrations in Ontario Dairy Cows
Bibliographic record
Abstract
All DHI test-day data, including milk urea concentrations measured by 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 objectives of this study were to describe the relationships between milk urea concentrations and seasonal factors, sampling factors, cow factors, and test-day production of milk, milk fat, protein, and SCC. Milk urea was associated with month and season; concentrations were the highest from July to September. Milk urea was generally lower in first-lactation cows. Milk urea was lowest during the first 60 d of lactation, higher between 60 and 150 d in milk, and lower after approximately 150 d in milk. In herds on an alternating a.m./p.m. test schedule, milk urea was generally lower in a.m. than p.m. tests. There was a positive nonlinear association between milk urea and milk yield, fat-corrected milk, and energy-corrected milk. There was a negative nonlinear association between milk urea and both milk fat and total protein percentages. While there was a negative nonlinear association between cow-level milk urea and linear score, the study found no association between herd average milk urea and herd average linear score. The associations described in this study using Dairy Herd Improvement test-day samples from commercial dairy herds and using an infrared test to measure milk urea are generally consistent with results from studies that used individual animals housed under research conditions and chemical methods to measure milk urea. Because milk urea varies by season, month, parity group, stage of lactation, and sample type, studies should control for these variables. Because of the apparent effect of a.m. and p.m. sampling on urea concentration, producers on an alternating a.m./p.m. test schedule should test routinely to establish a herd pattern for urea and submit the same sampling time consistently or both.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".