Correlations between fat depot traits and fatty acid composition in abdominal subcutaneous adipose tissue and longissimus muscle: Results from a White Duroc × Erhualian intercross F2 population1
Bibliographic record
Abstract
The aim of this study was to quantify the partial correlation coefficients (r(p)) between fat depot traits (FDT) and the fatty acid composition of abdominal subcutaneous adipose tissue and LM intramuscular fat in 639 F(2) pigs derived from a White Duroc × Chinese Erhualian cross. Fat depot traits are classified into 2 groups: 1 is adipose tissues (abdominal subcutaneous adipose tissue weight, mesenteric adipose tissue weight, perirenal adipose tissue weight, and backfat thickness at 4 locations); the other is LM [intramuscular fat content (IMF) and marbling score]. Correlations of FDT within classification groups were markedly greater (P < 0.001) than those observed between the 2 groups (r(p) = 0.62 vs. 0.26), indicating variability in fat content of muscle is relatively independent of amount of carcass fat. In general, fatter pigs had greater (P < 0.05) proportions of SFA and MUFA, and less PUFA, than leaner pigs. However, the relationships of individual fatty acids with FDT varied. We found that the amounts of some fatty acids regarded as neutral (e.g., stearic acid) or beneficial (e.g., palmitoleic acid and linolenic acid) for human health were associated with smaller amount of adipose tissues, or merely with greater IMF (P < 0.05). Therefore, we conclude that increasing the proportions of these neutral or healthy fatty acids can be achieved without reducing the IMF of LM, which is positively related to eating quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".