Exploring factors associated with bulk tank milk urea nitrogen in Central Thailand
Bibliographic record
Abstract
AIM: The study was to determine seasonal fluctuations and non-nutritional factors associated with bulk tank milk urea nitrogen(BTMUN). MATERIALS AND METHODS: A total of 58,364 BTM testing records were collected from 2364 farms in Central Thailand during September 2014-August 2015. Using square root BTMUN as the outcome, other milk components, farm effect, and sampling time were analyzed by univariable repeated measures linear regression, and significant variables were included in multivariable repeated measures linear regression. RESULTS: The average BTMUN (standard deviation) was 4.71 (±1.16) mmol/L. In the final model, BTM fat and protein percentages were associated with BTMUN as quadratic and cubic polynomials, respectively. BTM lactose percentage and the natural logarithm of somatic cell counts were negatively linearly associated with BTMUN. At the farm level, the BTM lactose association was negatively linear; herd BTMUN decreased following an increase of herd lactose average, and BTM lactose slopes were quite different among farms as well. Sampling time had the highest potency for the estimation of BTMUN over time, with lows and highs occurring in August and October, respectively. The variation in test level BTMUN was decreased by 18.6% compared to the null model, and 6% of the variance could be explained at the farm level. CONCLUSION: The results clarify seasonal variation in BTMUN and the relationships among other BTM constituents and BTMUN, which may be useful for understanding how to manage lactating dairy cattle better to keep BTM constituents within normal ranges.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".