Effects of presynchronization and postinsemination treatments on pregnancy rates to a timed breeding Ovsynch protocol in dairy cows and heifers
Bibliographic record
Abstract
This study examined two strategies to improve pregnancy rate (PR) following Ovsynch timed artificial insemination (TAI) for lactating dairy cows (n = 225) and nulliparous heifers (n = 87). Animals were assigned randomly to receive one of three treatments: Ovsynch protocol (GnRH 7 d before and 48 h after one PGF2α treatment), Presynch + Ovsynch (two treatments of PGF2α 14 d apart followed by Ovsynch 14 d later), or Ovsynch + Post-AI GnRH (GnRH 6 d after Ovsynch TAI) for first service breeding. Pregnancy rates among treatments were not different in lactating cows (42.5, 48.0, and 44.9%) or heifers (65.5, 58.6, and 58.6%) for Ovsynch, Presynch + Ovsynch, and Ovsynch + Post-AI, respectively. Cows treated with Ovsynch had lower PR when bred < 76 d in milk (DIM) compared with Presynch + Ovsynch or Ovsynch + Post-AI treatments. In addition, cows and heifers that received Post-AI GnRH had greater progesterone (P4) concentrations on day 21 and day 28 post-TAI than the Ovsynch group. Animals with higher P4 concentrations at initiation of Ovsynch had better PR than those with low P4 concentrations. Presynch animals had a greater proportion of animals with P4 values above 1 ng mL-1 at the initiation of Ovsynch than those animals in the Ovsynch group (74.5 vs. 59.4%). Heifers had lower PR if they were <14.6 mo of age (48.9 vs. 75%) or weighed <380 kg (47.4 vs. 70.8%). Although no significance differences in PR were observed between treatments in cows or heifers, DIM in cows and age and weight in heifers affected PR.Key words: Ovsynch, presynchronization, gonadotropin-releasing hormone, dairy heifer, dairy cow, timed artificial insemination
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".