Effect of Insemination–Ovulation Interval and Addition of Seminal Plasma on Sow Fertility to Insemination of Cryopreserved Sperm
Bibliographic record
Abstract
In swine, the use of frozen-thawed (FT) sperm for artificial insemination (AI) is limited because of poor sow fertility, possibly associated with a post-thaw capacitation-like status resulting in fewer fully viable sperm. Sow fertility to AI with FT sperm may improve with deeper deposition of sperm within the female tract, insemination very close to ovulation, or reversal of cryocapacitation by seminal plasma (SP). We performed two experiments to examine these suggestions. In experiment 1, 122 multiparous Yorkshire sows received 600 IU equine chorionic gonadotrophin at weaning and 5 mg pLH 80 h later to control time of ovulation. The predicted time of ovulation (PTO) was 38 h after pLH injection. Thereafter, sows were assigned on the basis of parity to a single AI of FT sperm at 2 h before PTO, or at 12 h before PTO, or FT sperm supplemented with 10% SP at 12 h before PTO. Control sows received fresh semen at 12 h before PTO. All semen doses were adjusted to 3 x 10(9) live cells and deposited into the cervix. Experiment 2 employed 99 multiparous crossbred sows and repeated the treatments of experiment 1 except that all FT inseminations were intrauterine. In both experiments, farrowing rates were lower (p < 0.01) following FT inseminations with no effect of time of insemination or of supplemental SP. In experiment 1, litter size was smaller following FT insemination (p < 0.05), but no effect on litter size was evident in experiment 2. Supplemental SP had no effect on litter size in either experiment. The lack of effect of either SP or timing of FT insemination on sow fertility suggests that the non-lethal sperm cryoinjury affecting fertility involves more than just cryocapacitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| 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) | 0.000 | 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 teacher head, 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".