Impact of Paternity Errors in Cow Identification on Genetic Evaluations and International Comparisons
Bibliographic record
Abstract
The impact of paternity identification errors on US genetic evaluations and international comparisons of Holstein dairy bulls for milk, fat, and protein yields was investigated. Sire identification was replaced for 11% of Holstein cows that were sired by AI bulls and had records in the US database for national genetic evaluations; US evaluations were computed based on those modified pedigrees and compared with official national evaluations. Estimated breeding values from the data with introduced paternity errors were biased, especially for later generations. Estimated genetic trends decreased by 11 to 15%. Estimates of standard deviations of sire transmitting ability also decreased by 8 to 9%. International multitrait across-country comparisons of bulls were computed based on national evaluations from the United States, Canada, New Zealand, and The Netherlands. Estimates of genetic correlations between the United States and other countries decreased by 0.04 to 0.06 when US evaluations were based on modified pedigree. The resulting bias toward selection of domestic bulls and the inability to identify truly superior animals that are available internationally could decrease potential selection differentials by 0.07 to 0.09 standard deviation units on the US scale, which corresponds to sire breeding values of approximately 50 kg for milk, 3 kg for fat, and 1.7 kg for protein. Losses for the other countries were lower and ranged from 0.02 to 0.05 standard deviation units, because a correlation of less than unity with the United States decreased the impact of US cow paternity errors on the scales of other countries. Although paternity verification is desirable and technically feasible, commercial implementation would require low testing costs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".