Prevalence and Risk Factors for Postpartum Anovulatory Condition in Dairy Cows
Bibliographic record
Abstract
The objectives of this research were to determine the prevalence of the anovulatory condition within a temperate region of North America and identify cow-level and herd-level risk factors for this condition. A total of 1,341 cows from 18 herds were classified as cycling or anovular based on skim milk progesterone concentration determined at 46 and 60 +/- 7 d in milk. Calving history, periparturient disease incidence, body condition score, milk ketone concentration in the first 2 wk of lactation, and first 305-d mature-equivalent milk projections were recorded. Reproductive and culling information was retrieved monthly from the Dairy Herd Improvement Association. The cow-level prevalence of anovulation was 19.5%, with a herd-specific range from 5 to 45%. Accounting for the effect of clustering at the herd level, cows experiencing a difficult calving, cows with twin calvings, displaced abomasum, and cows with subclinical ketosis in the first week after calving were at greater risk for diagnosis of anovulation. Anovular cows within herds using ovulation synchronization programs were inseminated at the same time postpartum with a 6-percentage point reduction in the probability of pregnancy relative to cycling herdmates (29.7 vs. 35.9%, respectively), whereas anovular cows in herds breeding based on observed estrus were inseminated 8 d later and suffered a 10-percentage point reduction in the probability of pregnancy at first insemination (20.3 vs. 30.5). Time to pregnancy was delayed in anovular cows by 30 d (156 vs. 126 d). Using survival analysis, the impact of anovulation decreased with time. The daily probability of pregnancy (hazard ratio) was similar to cycling cows by 165 d in milk. The results underline the important associations of peripartum health with reproductive function and 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 | 0.000 | 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 | 0.000 | 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".