Factors associated with milk pregnancy-associated glycoprotein levels in pregnant dairy cows carrying pregnancies to term and their interaction with milk yield at test day
Bibliographic record
Abstract
The interpretation of milk pregnancy-associated glycoproteins (PAG) ELISA tests can potentially be improved by understanding the variations in PAG levels throughout gestation. We hypothesized that milk yield (MY) on test day has a meaningful effect on these variations, but that other factors may influence this relationship. The objective of our study was to investigate and quantify the interactions between test-day MY and parity and breed, considering the known fluctuations in PAG levels as a function of the number of days in pregnancy (DIP). Milk samples (n = 272,042) submitted to the Lactanet (Canadian Network for Dairy Excellence, Canadian DHIA program) laboratory from 2013 to 2021 for pregnancy testing using a commercial PAG ELISA test, conducted between 25 and 210 DIP, were compared with records of insemination and calving dates to identify pregnant cows at testing. Only cows with a confirmed calving were included and a multivariable model was built with PAG ELISA value as the outcome variable and DIP, MY at testing, parity, breed, and their interactions included as fixed-effect independent variables. To account for variability at the herd and cow levels, the herd and cow identification variables were incorporated as random effects. Pregnancy-associated glycoprotein fluctuations relative to DIP were consistently observed at all levels of these variables. High-yielding, multiparous, and Holstein cows exhibited lower milk PAG levels. Even at the same threshold of MY, multiparous cows had lower PAG levels compared with primiparous cows, and Holstein cows had lower PAG levels compared with Jersey and Ayrshire cows, particularly in early gestation, thus confirming the age and breed effects beyond their associations with MY. Lesser milk PAG levels in multiparous and Holstein cows can be partially, but not entirely, attributed to their higher MY. Beyond MY, parity and breed help further explain PAG variations through gestation. Although these variables effects are unlikely to misclassify pregnancy under most conditions, accounting for them may improve the interpretation of pregnancy diagnosis with this test.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".