Pork Production with Entire Males: Directions for Control of Boar Taint
Bibliographic record
Abstract
Boar taint is caused by the accumulation of androstenone and skatole and other indoles in the fat; this is regulated by the balance between synthesis and degradation of these compounds and can be affected by a number of factors, including environment and management practices, sexual maturity, nutrition, and genetics. Boar taint can be controlled by immunocastration, but this practice has not been accepted in some countries. Genetics offers a long-term solution to the boar taint problem via selective breeding or genome editing. A number of short-term strategies to control boar taint have been proposed, but these can have inconsistent effects and there is too much variability between breeds and individuals to implement a blanket solution for boar taint. Therefore, we propose a precision livestock management approach to developing solutions for controlling taint. This involves determining the differences in metabolic processes and the genetic variations that cause boar taint in specific groups of pigs and using this information to design custom treatments based on the cause of boar taint. Genetic, proteomic or metabolomic profiling can then be used to identify and implement effective solutions for boar taint for specific populations of animals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".