Bulk tank milk urea nitrogen: seasonal patterns and relationship to individual cow milk urea nitrogen values.
Bibliographic record
Abstract
The objectives of this study were: 1) to determine if bulk tank milk urea nitrogen (BTMUN) and whole herd weighted average of the individual cow MUN levels (WHMUN) were equivalent measurements of herd MUN status; and 2) to determine the seasonal variation in BTMUN concentrations in Prince Edward Island (PEI) dairy herds. For BTMUN-WHMUN correlation testing, bulk tank milk samples from 176 herds were tested for MUN once every 1 to 2 wk between September 1999 and August 2002, as part of routine BTM testing for milk components. During this 3-year period, all herds had all milking cows tested for MUN once a month at the same lab. The WHMUN levels (weighted for milk production) were calculated for each month, and were compared to BTMUN levels using a concordance correlation coefficient (CCC) and a graphic procedure. Tests were only compared if they occurred on the same date, producing a final dataset of 669 comparisons. The BTMUN had good (but not perfect) correlation with WHMUN (CCC = 0.91). This high reliability extended to both the pasture and non-pasture seasons, various milk sampling protocols, and all herd sizes seen in PEI. For evaluating the seasonal variation of BTMUN, the 3 y worth of data (24 803 observations) were divided into 15 seasonal categories, 5 seasons per year (early, mid, and late pasture, and early and late stable). Using linear mixed modelling, significantly (P < 0.05) higher BTMUN values were found during the mid and late pasture seasons of 2000, likely because the precipitation was unusually high during this period, enhancing pasture growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".