DNA-based Paternity Analysis in Multi-bull Breeding Programs on Beef Cattle Operations in Western Canada
Bibliographic record
Abstract
A 3-yr study was conducted to evaluate deoxyribonucleic acid (DNA)-based paternity analysis on commercial beef ranches managing multi-bull breeding systems. Five commercial ranches in central Saskatchewan Canada participated in the study with a total of 22 breeding groups. All bulls (n = 75) and calves (n = 2243) were sampled to determine parentage. Number of calves sired per bull ranged from 1 to 87 (23 ± 15.9). The value of a calculated index of bull prolificacy (BPI) ranged from 0.05 to 3.83. Older bulls had a BPI averaging 1.10, 2-yr old bulls 1.00, and yearling bulls 0.76 (p > 0.05). Strong positive (r = 0.93, n = 74, p = 0.01) correlation was observed between total calves born per bull and calves born in the first 21 days BPI, between total calves born per bull and calves born in week-3 BPI (r = 0.69, n = 74, p = 0.01). Bull age was shown to play a significant role when determining prolificacy, with older bulls siring more calves than younger bulls. Bull number per breeding group influenced the number of calves sired. As number of bulls per breeding group increased so did the variation in the number of calves sired by each bull. Conducting DNA parentage testing only on calves born in the first 21 d or in week 3 of the calving season may provide an opportunity to decrease costs and turn-around time for laboratory results and decisions made, prior to the next breeding season.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".