Association between genomic daughter pregnancy rates and reproductive parameters in Holstein dairy cattle
Bibliographic record
Abstract
The objective of this observational study was to evaluate the relationship between genomic daughter pregnancy rate (GDPR) with reproduction parameters such as pregnancy at first artificial insemination (AI), pregnancy per AI, and pregnancy losses (PL). A total of 12,949 events from 3,499 Holstein cows were included. Cows were enrolled as nulliparous (n = 1,220), primiparous (n = 1,314), or multiparous (n = 965). Cows were bred either after a timed AI protocol, timed embryo transfer (ET), or spontaneous estrus. Most lactating cows were bred following a timed AI protocol based on estradiol and progesterone, and most nulliparous were artificially inseminated following estrus detection. Hair samples were collected from the tail switch and cows were genotyped using a SNP platform (Clarifide, Zoetis). Cows that were bred by timed AI were evaluated for estrous behavior using tail chalk. Tail chalk was applied on the head of the tail 2 d before timed AI and the chalk was evaluated at AI (no estrus: 100% of chalk remaining or ≥50% of chalk remaining; Estrus: <50% of chalk remaining). Pregnancy diagnosis was performed at d 32 and 60 after AI using ultrasonography, and the presence of a heartbeat was considered a positive diagnosis. Pregnancy loss was defined as a pregnant cow on d 32 that was nonpregnant on d 60. As GDPR increased, the odds of pregnancy at first AI increased [odds ratio (OR) = 1.28, 95% CI = 1.20-1.35], the odds of pregnancy per AI increased (OR = 1.31, 95% CI = 1.25-1.36), and the odds of PL decreased (OR = 0.66, 95% CI = 0.60-0.72). Most cows that were bred on the day of the timed AI demonstrated estrus (n = 6,075; 92.9%). The odds of demonstrating estrus on the day of timed AI increased as GDPR increased (OR = 1.31, 95% CI = 1.17-1.48). There was no interaction between GDPR and parity or breeding management for pregnancy at first AI, pregnancy per AI, and PL. In conclusion, the odds of pregnancy at first AI and pregnancy per AI increased as GDPR increased. Moreover, the odds of PL increased as GDPR decreased. Greater GDPR was also associated with greater occurrence of estrus on the day of timed AI. These results suggest that selecting for higher GDPR could result in better reproductive performance, but this would need to be assessed with additional research.
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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 | 0.002 |
| 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) | 0.001 | 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".