Fertility after intrauterine insemination with conventional or low numbers of spermatozoa in sows with synchronized ovulation
Bibliographic record
Abstract
Objective: To determine sow fertility to a single timed intracervical or intrauterine insemination of conventional or low sperm numbers. Materials and methods: A total of 411 mixed-parity sows were subjected to controlled ovulation by injection of 600 IU equine chorionic gonadotrophin at weaning and 5 mg porcine luteinizing hormone (pLH) 80 hours later. Sows were assigned to a single insemination of 1 or 3 × 109 sperm delivered into either the cervix or uterus. Inseminations were performed approximately 36 hours after pLH injection. Intensity of standing estrus at insemination was subjectively scored as 1 to 3, with 3 being a stronger response, and semen backflow was recorded as yes or no. Results: Number of sperm and site of deposition did not affect pregnancy or farrowing rates or subsequent litter size. Mean farrowing rates were 68.32% and 68.63% in sows inseminated using an intrauterine catheter and either 1 or 3 × 109 sperm, respectively. In sows inseminated using the cervical method, farrowing rates were 77.88% and 67.31% when 1 and 3 × 109 sperm were used, respectively. Greater intensity of estrus at insemination was associated with higher pregnancy and farrowing rates (P < .001), and backflow during insemination was associated with lower pregnancy and farrowing rates (P < .01). Implications: When appropriately timed after induced ovulation, insemination of low sperm numbers does not adversely affect sow fertility, and this lack of effect is independent of the site of sperm deposition.
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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 | 0.000 |
| 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".