Origanum vulgare L. and Rosmarinus officinalis L. Aqueous Extracts in Growing-finishing Pig Nutrition: Effects on Antioxidant Status, Immune Responses, Polyphenolic Content and Sensorial Properties
Bibliographic record
Abstract
The effects of an oregano and/or rosemary (Origanum vulgare L. and/or Rosmarinus officinalis L.) dietary supplementation to the diet of fattening pigs were investigated. Thirty-two grower-finisher pigs (45 kg) were divided into four dietary groups identified as: control diet (CTR); CTR+ 0.2% oregano (O); CTR + 0.2% rosemary(R), and CTR+ 0.1% oregano + 0.1% rosemary (OR). During the finishing period, all groups received a further supplementation of 0.5% of conjugated linoleic acids (CLA). Blood samples were collected after an adaptation period of 15 days to the new diet (T1) and at the end of the finishing period (T2) to evaluate antioxidant status (total antioxidant power and reactive oxygen metabolites) and immune responses (lymphocytic phenotyping and IgG levels). Pork meat samples were evaluated for glutathione peroxidase activity (GSHPx), total phenolic content and preference rating. A significant increase in B lymphocytes (CD79+) and a higher IgG level was observed in the R and O groups (P<0.05). Furthermore, there were significant effects of dietary supplementation on meat GSHPx activity and total phenolic contents (P<0.001 and P<0.005, respectively). Preference rating showed that pork derived from group R was the most preferred by the 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) | 0.001 | 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 | 0.000 |
| 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".