Enrichment with<i>n</i>-3 fatty acid by tuna oil feeding of pigs: changes in composition and properties of bacon and different sausages as affected by the supplementation period
Bibliographic record
Abstract
Khiaosa-ard R., Chungsiriwat P., Chommanart N., Kreuzer M. and Jaturasitha S. 2011. Enrichment with n-3 fatty acid by tuna oil feeding of pigs: changes in composition and properties of bacon and different sausages as affected by the supplementation period. Can. J. Anim. Sci. 91: 87–95. Belly, lean from the shoulder and backfat obtained from 80 pigs, fed either no or 1.6 kg tuna oil during fattening (35–90 kg body weight), were used to prepare bacon, Chinese-style sausage and Vienna-style sausage. The tuna oil had been supplemented either initially, at the end, or continuously during fattening. In all meat products, tuna oil supplementation clearly increased contents of n-3 fatty acids (FA), especially of the long-chain n-3 FA. Differences among supplementation periods were pronounced only in the n-3 FA proportion of total FA being lower with early tuna oil feeding. Thiobarbituric acid value, which was high in dry Chinese-style sausage, was mostly enhanced by tuna oil, whereas the period of tuna oil supplementation had no systematic influence. In conclusion, any mode of tuna oil supplementation investigated was efficient in enriching n-3 FA, but care should be taken in producing dry sausages due to their susceptibility to rancidity during storage time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".