Genetic evaluation for piglet crushing behaviour in primiparous sows
Bibliographic record
Abstract
Stress in farrowing sows is associated with the number of piglets crushed or attacked. Sow’s behaviour is variable and heritable, therefore genetic selection can be a viable approach for improving pig’s welfare. In this report, we used first parity litter records of Yorkshire sows to test a genetic evaluation model for piglet crushing. The data were split into training and validation to check the prediction accuracy of piglet crushing estimated breeding values (EBVs) for young sows. We found that the estimated heritability of piglet crushing was 0.07 ± 0.03. The difference in the EBVs in the validation set was equivalent to 0.15 more piglets crushed in the top 10% group than in the bottom group of sows. These results indicate that the genetic selection may be used to reduce piglet crushing which will improve the welfare of pigs as well as production efficiency. The average reliability of the estimated EBVs across all animals in the pedigree was (0.07; 0.0 to 0.72). More research on evaluation models and the genetics underlying sow stress and behaviour is warranted to improve the reliabilities of modeling and to identify robust genetic markers for animal breeding for the implementation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".