Bibliographic record
Abstract
Summary: Raising intact males is a promising possibility in the ongoing quest for leaner hogs.This paper reviews the advantages and problems of rearing intact males, including an overview of boar taint, and outlines a practical feeding approach for split-sex feeding in operations that chooseto rear intact males. This decadewill be known as the decade of lean growth in the swine industry, as both carcassesand economic returns become more lean. The United States swine industry now realizes the importance of leaner carcasses.In Canada,where we rarely dispute the benefits of our 20-year-oldnational hog grading system, we are reevaluating whether this system has provided enough of an incentive for a fat- instead of a lean-genotype hog. In our trend towards the lean carcass, our options are limited. We can: restrict feed; genetically select for leanness, a slow process; buy lean pork from overseas; use porcine somatotropin (PST) injections to achieve a leaner hog; or raise intact males. PSTis probably the most cost-effective way to increase lean yield, but it may not be launched onto the market. With or without PST,the North Americanswine industry is losingfeed efficiency and average daily gain (ADG)by failing to rear intact males. Boar tai nt The most serious disadvantage of rearing intact males (especially to the slaughter weights typical in North America) is boar taint. Boar taint is caused primarily by the compounds androstenonel and skatole;2however, sensory studies carried out by Bonneau3suggest that there are other contributing compounds.These compounds are probably testicular in origin and are more likely to contribute to boar taint in the sexually mature male than in younger, lighter, or later-maturing boars.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.628 | 0.550 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".