85 DNA parentage in multi-sire breeding groups and sire repeatability on replacement heifer performance
Bibliographic record
Abstract
Abstract The objective of this study was to apply DNA parentage testing in multi-sire breeding groups to assess repeatability of a sire’s impact on replacement females and terminal animals. Parentage testing was performed on DNA from 37 bulls and 1578 calves from a commercial ranch using natural breeding in four multi-sire breeding pastures over a four-year period (2015–2018). Parentage data was analyzed using Chi-square procedures. In 2016 and 2018, all bulls in the four breeding groups sired significantly different (P < 0.01) numbers of calves than expected. In 2015 and 2017, bulls in 2 of 4 and 3 of 4 breeding groups, respectively, sired significantly different (P < 0.01) numbers of calves than expected. For the 2015 and 2016 calf crops, three of 24 bulls used sired 30% of the calves and 33% of the selected replacement heifers. In 2017 and 2018, the calves born to the daughters of these three most prolific sires, averaged 4.5kg lower weaning weights than the average wean weights of the remaining calves. In 2018, all calves were tested for breed composition and vigour score using the EnvigourHX test from Neogen (Neogen Canada Inc., Edmonton Alberta, Canada). The average herd vigour score was 70%; the average vigour score for 2018 calves born to daughters of the top three prolific sires was 56%. Coupling production measures (weaning weights, birth dates, calving intervals) with sire parentage and vigour score data provides beef cattle producers with additional information to guide breeding choices to improve production in their operations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".