Can We Prevent Boar Taint and Reduce the Need to Castrate Male Pigs?
Bibliographic record
Abstract
Abstract Castration of male piglets to prevent boar taint was traditionally done without pain relief. Current codes of practise require the use of analgesics, but castration still results in some pain and stress, increased chance of infections and decreased production efficiency, so alternatives to castration for controlling boar taint are needed. Boar taint is caused by androstenone, a sex pheromone produced by the testis, and skatole, which is produced from the degradation of tryptophan by the gut microbiota. Boar taint is a multifactorial issue that is influenced by numerous physiological processes that vary between different breeds and individuals, with some pigs exhibiting naturally low levels of boar taint. This case study evaluates strategies to identify those individuals with a low potential for boar taint, which would allow them to be used in pork production without further treatment. Levels of plasma androstenone at 21 and 28 days of age were the most reliable predictors of boar taint at maturity. For pigs with a high potential for boar taint, strategies to control boar taint without castration that do not result in decreased production efficiency are needed. This includes immunocastration and genetic selection; nutritional strategies using prebiotics and dietary binding agents are also possible. Information © The Authors 2025
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".