Relative contribution of breed, slaughter weight, sex and diet to the fatty acid composition of differentiated pork
Bibliographic record
Abstract
The objective of this study was to investigate the relative contribution of breed composition, slaughter weight, sex, diet, and their interactions to the fatty acid composition of intramuscular and subcutaneous fat of pigs. Sires from Duroc, Lacombe, and Iberian breeds were crossed to Large White × Landrace dams and offspring (barrows and gilts) were randomly allocated into three feeding groups (Control, Canola or Flax) 3 wk before slaughter, aiming at slaughter weights of either 115 or 135 kg. In intramuscular fat, dietary treatment (88.7%) was responsible for most of the explained variability observed in 18:3 n-3 (0.76), followed by breed and the breed × diet interaction. In subcutaneous fat, the same factors contributed for the explained variance in 18:3 n-3 (0.84) in a similar order. Furthermore, diet contributed more than 94% to the explained variability observed in n-6/n-3 (0.90). On the other hand, both for the intramuscular and subcutaneous fat, breed was the most influential factor (68.9%/68.2%, respectively) for the explained variance in 18:2 n-6 (0.38/0.59, respectively). Both sex and slaughter weight also had significant effects (P < 0.05) on some individual fatty acids and indices. Understanding the contribution of each factor and their interactions will help the pork industry in the production of consistent differentiated products.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".