Effect of pregnancy on production traits of Canadian Holstein cows
Bibliographic record
Abstract
Seven test-day models with different ways of accounting for the effect of pregnancy on production traits were compared by their residual variance, rank correlations of estimated breeding values of bulls and cows and number of nonpregnant cows in the top 500 for milk yield and milk persistency. Data were 22,546,696 first-parity test-day milk, fat, and protein yields and somatic cell score records of 2,677,862 Canadian Holstein heifers calved between 1988 and 2006. The first model fitted separate lactation curves to 8 days open classes and 1 curve to a nonpregnant cow class. Two other models adjusted for pregnancy by fitting the effect of month of pregnancy or stage of pregnancy. One model fitted regression on days pregnant. The remaining 3 models fitted interactions between stage of pregnancy and stage of lactation when conception occurred using either regression on days pregnant nested within days open or classes for specific stage of pregnancy and stage of lactation combination. All models were contrasted to a model without any adjustment for the effect of pregnancy. Both models that accounted for the effect of pregnancy and the model without the effect of pregnancy had similar residual variance. Adjusting for the effect of pregnancy did not cause reranking of sires for estimated breeding values for 305-d yield and persistency but influenced ranking of cows. Models that used days open for the effect of pregnancy overestimated breeding values of nonpregnant cows and cows with shorter days open. No interaction was found between stage of pregnancy and stage of lactation. Month of pregnancy and stage of pregnancy models, compared with the model without the effect of pregnancy, decreased overestimation of breeding values of nonpregnant cows and did not overestimate breeding values of cows with short days open like models fitting days open. Month of pregnancy and stage of pregnancy models are recommended for estimation of adjustment factors for the effect of pregnancy on production traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".