253 Sire verification in multi-sire breeding systems
Bibliographic record
Abstract
The objective of this study was to evaluate the use of DNA parentage testing on commercial cow-calf operations using multi-sire breeding pastures and to determine associations between phenotypic and spermatological traits of bulls and number of calves sired. Seven breeding pastures located within 4 commercial Saskatchewan ranches cooperated in this study. Calves and bulls were DNA parentage tested to determine sires. Data were analyzed using Chi square procedures. Bulls sired a significantly different (P < 0.01) number of calves compared to expected in 5 of the breeding pastures. Bull age was found to significantly (P < 0.01) affect bull prolificacy. All bulls were required to pass a breeding soundness exam (BSE) before entering a breeding pasture, so no association was found between either scrotal circumference (R2 = 0.04) or percent normal sperm (R2 = 0.13). Economic models were developed to evaluate the value of adopting this technology on farm. One model showed that bulls who sired more calves had a lower cost per calf sired. Another model showed that using parentage testing to identify bulls causing dystocia, by testing calves from difficult births and then culling the responsible bull, can provide an economic return on investment to the farm. Results also show that a producer could reduce testing costs by up to 70% by only testing calves born in week 3 and still obtain results that correctly identify low and high prolificacy sires. Only testing a sample of the calf crop also ensures lab results are obtained in time to make changes to the bull battery ahead of the next breeding season. Real value from parentage testing comes from being able to couple sire parentage with other basic production records. There is potential to increase overall bull prolificacy in a herd and increase other economically important traits by using DNA parentage to aid in sire selection. Study results indicated a large variation in number of calves sired by each bull in multi-sire breeding pastures. Although some phenotypic and spermatological factors can be correlated with bull prolificacy, the large variation in number of calves sired suggests that these traits alone cannot be relied on to predict bull prolificacy. If basic production records are kept on farm, there is an economic incentive to adopt this technology.
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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 | 0.006 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".