S<scp>hort</scp> C<scp>ommunication</scp>: Associations of <i>BoLA</i> alleles <i>DRB3.2*16</i> and <i>DRB3.2*23</i> with health-related traits in Holstein bulls
Bibliographic record
Abstract
Sharma, B. S., Verschoor, C. P. and Karrow, N. A. 2011. Short communication:Associations of BoLA alleles DRB3.2*16 and DRB3.2*23 with health-related traits in Holstein bulls. Can. J. Anim. Sci. 91: 597–600. The relationships between bovine leukocyte antigen (BoLA) DRB3.2 alleles and health and fertility traits were investigated. A group of 548 Canadian and American Holstein bulls was genotyped for the presence of DRB3.2*16 and DRB3.2*23 alleles using the multi-primer target polymerase chain reaction technique. The traits of interest included somatic cell score (SCS), lactation persistency, daughter fertility (DF), and herd life (HL). Higher frequencies were observed for alleles DRB3.2*16 and DRB3.2*23 in this bull population compared with previous reports. In a comparison-wise level, some significant contrasts were detected; however, no association was detected between the DRB3.2 alleles and SCS. Allele DRB3.2*16 had a favorable effect on HL compared with allele DRB3.2*23. On the other hand, these two alleles had a favorable influence on DF, additionally, individuals carrying both of these two alleles performed better than the individuals carrying either allele. Further investigation is warranted to examine the effects of these alleles on HL and reproduction performance.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.009 |
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".