Evaluating the quality of feed fats and oils and their effects on pig growth performance
Bibliographic record
Abstract
Feed fats and oils provide significant amounts of energy to swine diets, but there is large variation in composition, quality, feeding value, and price among sources. Common measures of lipid quality include moisture, insolubles, and unsaponifiables (MIU), titer, and free fatty acid content, but provide limited information regarding their feeding value. Lipid peroxidation is an important quality factor related to animal growth performance and health, but maximum tolerable limits in various lipids have not been established. Several indicative assays can be used to detect the presence of various peroxidation compounds, but due to the complexity and numerous compounds produced and degraded during peroxidation process, no single method can adequately determine the extent of peroxidation. Until further information is available, using a combination of peroxide value, thiobarbituric acid reactive substances (TBARS), and anisidine value appear to provide a reasonable assessment of the extent of peroxidation in a lipid at a reasonable cost. However, fatty acid composition of the lipid being evaluated should be considered when selecting specific assays. Predictive tests can also be used to estimate the stability or susceptibility of lipids to peroxidation and include active oxygen method, oil stability index, and oxygen bomb method. A review of 16 published studies with pigs has shown an average decrease of 11.4% in growth rate, 8.8% feed intake fed isocaloric diets containing peroxidized lipids compared to diets containing unperoxidized lipids of the same source. Furthermore, serum vitamin E content was generally reduced and serum TBARS content was increased when peroxidized lipids were fed in these studies, suggesting that feeding peroxidized lipids negatively affects metabolic oxidative status of pigs. However, it is unclear if antioxidants are useful additions to lipids to maintain optimal nutritional value, or if their addition to swine diets is beneficial in overcoming a metabolic oxidative challenge.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".