Phenotypic analysis of pregnancy effect on milk, fat, and protein yields of Canadian Ayrshire, Jersey, Brown Swiss, and Guernsey breeds
Bibliographic record
Abstract
Pregnancy has a negative impact on milk production in dairy cattle. Estimates of the effects of pregnancy are required in genetic evaluation models. Test-day records of Ayrshire, Jersey, Brown Swiss, and Guernsey breeds were analyzed phenotypically for the effect of pregnancy using 4 different models. Milk, fat, and protein yields were analyzed separately. The first model used a fourth-order Legendre polynomial regression on days in milk within classes of 10 d open. The second model fitted stage of pregnancy within days open classes to investigate the possible interaction between lactation stage and gestation stage. The third model included a fourth-order Legendre polynomial regression on days pregnant. In the fourth model, test-day records were divided into stage of pregnancy classes. Given that the effect of pregnancy was significant for all models, and that the adjusted R-squared values were consistent across the models, implying that the models for each trait fitted equally well within breeds, models were therefore compared based on the practicality of the results. Analysis of the first model indicated that milk production for cows with < or =180 d open tended to have low yields in the last part of lactation. Cows with longer days open, however, had proportionally higher milk yield throughout lactation, suggesting a possible confounding effect of production level with days open effects. Results from the analysis involving the second model illustrated that there was no apparent interaction between lactation stage and gestation stage. Results from the third and fourth models showed that milk and fat yields began to decline after about 4 mo of pregnancy for all breeds, and protein yield began to decline after about 2 mo of pregnancy for all breeds. A lack of records during the final 60 d of pregnancy (the typical dry period) severely limited the third model, as pregnancy effects could not be estimated accurately. This problem was lessened, however, with the fourth (stage of pregnancy) model, because test-day records for cows > or =210 d pregnant were grouped together, allowing for a moderate number of test-day records in the final class of days pregnant. Because the stage of pregnancy model showed a decline in production that increased as gestation progressed, and because there was not a lack of test-day records at the end of pregnancy, the fourth model provided the most realistic estimate of the effect of pregnancy on milk production. Further investigation is needed into the incorporation of stage of pregnancy effects into genetic evaluations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".