The effect of non-nutritional factors on milk urea nitrogen levels in dairy cows in Prince Edward Island, Canada
Bibliographic record
Abstract
We determined the effects of non-nutritional factors such as breed, parity, days in milk (DIM), milk production, milk quality and milk components on milk urea nitrogen (MUN) concentration. A total of 177 dairy farms in Prince Edward Island containing 10,688 lactating dairy cows participated in the project. Individual-cow milk samples (n=68,158) were collected monthly from July 1999 to June 2000 from each farm. MUN was measured using a Fossomatic 4000 Milkoscan Analyzer at the PEI Milk Quality Laboratory. Descriptive statistics for MUN, parity, DIM, and test-day milk yield, fat and protein were calculated. Mixed linear-regression models were used; "cow" and "herd" were included as random effects to control for the effect of clustering of MUN test dates within cow, and clustering of cows within herd, respectively. The MUN was lower during the first month of lactation, peaked at 4 months of lactation, and decreased later in lactation. A positive relationship existed between MUN concentration and milk yield, while negative relationships with milk protein% and linear score were observed. A quadratic relationship existed between milk fat% and MUN concentration, with higher MUN occurring at mid-range fat percentages. The variation at the herd and cow levels in the model were 19.7 and 19.0%, respectively; while the variation at the test date level was 61.3%. The non-nutritional factors studied explained 13.3% of the variation in MUN.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".