Healthy Pork Production through Dietary n6:n3 Ratio Regulation
Bibliographic record
Abstract
The meat fat fatty acid composition could influent consumer health. Thus, this study was though dietary n-6:n-3 ratio regulation to production healthy pork. The experiment was used eighty LYD pigs that average weight was 66.5 kg (half male and half female) divide into five groups, they are lard group (L), soybean oil group (SO), commercial fish product group (CFP), canola oil group (CO), and 50% fish oil and 50% canola oil group (FCO) with 4 replicates, this experiment was lasted for 90 days. Experimental results indicated that the growth performances was no difference among groups; serum cholesterol, LDL and LDL-C were lower, meanwhile HDL was higher (P < 0.05) in FCO group than in control group. Back fat thickness, pork color, water holding capacity and meat fat content show no difference among groups. TBARS test on pork storage for 15 days in SO group was significantly higher (P < 0.05) than CO group. The n-6:n-3 ratio of back and belly fat in CFP, FCO and CO groups were significantly lower (P < 0.05) than in lard group. Panel evaluation score in SO group was significantly better (P < 0.05) than CO group in flavor, texture, juicy and total acceptance in longissmus muscle, but no difference in belly meat. In conclusion, the pork n-6: n-3 ratio was decreased with CFP, FCO, and CO supplementation, feeding pigs with low n-6:n-3 ratio fat could production healthy pork for consumers.
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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".