Effect of estrogen formulation and site of deposition on fertility of artificially inseminated sows treated with human chorionic gonadotrophin to induce ovulation
Bibliographic record
Abstract
Objective: To determine the effect of estradiol, either added to extended semen or deposited onto the vaginal mucosa, on the reproductive performance of artificially inseminated sows. Materials and methods: At 80 hours after weaning, 227 mixed-parity sows received an intramuscular injection of 750 IU of human chorionic gonadotrophin (hCG) to induce ovulation. At 36 and 46 hours after hCG injection, sows exhibiting estrous behaviour (n = 198) were artificially inseminated with 3 x 109 spermatozoa in 80 mL extender. At the time of insemination, sows were sequ;entially assigned to receive 25 mg estradiol dissolved in the semen dose (E-semen; n = 66), 25 mg estradiol in an oil solution deposited onto the anterior vaginal mucosa (E-vag; n = 66), or no estradiol (Control; n = 66). Real-time ultrasound was used to determine pregnancy status 26 to 30 days after insemination. Pregnancy rates, farrowing rates, and subsequent total-born litter sizes were recorded. Results: Pregnancy rates were 97.0%, 92.4%, and 90.9%, farrowing rates were 89%, 92% and 89%, and litter sizes were 11.1, 10.8, and 10.5, for E-semen, E-vag, and Control, respectively. Differences were not significant (P > .5). These data indicate no benefit from supplemental estradiol at the time of insemination. However, any effect may have been masked by an improved performance of all treatment groups relative to the herd’s historical farrowing rate (75.7%). Implications: The breeding of sows following a controlled induction of ovulation may have the potential to improve sow fertility. However, under these conditions, supplemental estradiol provided no significant further benefit.
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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 | 0.000 | 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".