Genetic parameters of Vulva Traits and Impact of Vulva Scores on Gilts Culling in Large White Pigs
Bibliographic record
Abstract
Vulva morphologies represent significant traits in pig production. Recent studies suggest vulva size can be predictive of the reproductive performance of gilts. We aimed to analyze the genetic parameters of vulva traits, including vulva length (VL), vulva width (VW), and vulva angle score (VAS), as well as litter traits, including total number born (TNB), number born alive (NBA), number stillborn (NS), and piglet survival rate (SR), across three Large White pig strains (PIC, Topigs, and Canadian). We estimated the correlations between vulva and litter traits, as well as the reasons for culling gilts. The heritabilities of vulva traits ranged from 0.167 to 0.426, whereas the heritability of litter traits ranged from 0.013 to 0.147. The VAS in Topigs Large White pigs exhibited the highest heritability. The genetic correlation coefficients between vulva length and width in PIC and Topigs Large White pigs were significantly positively correlated, ranged from 0.585 to 0.767. No significant correlation was found between vulva and litter traits. Subsequently, we scored the vulva traits according to previously reported studies. The average vulva width score of the gilts that were culled due to prolonged estrus was significantly lower (2.75) compared to that of gilts with normal estrus (2.90). In the population of gilts aged 220 to 230 days, the gilts with higher vulva angle scores had a lower risk of culling due to vulva inflammation with purulent discharge. The results suggest that selecting for vulva traits in replacement gilts is an effective strategy to reduce gilts culling rates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".