Dietary oil rich in polyunsaturated fatty acids for ruminants: Post-ruminal digesta characteristics and their implications on production
Bibliographic record
Abstract
Maximizing the production of ruminants requires high intakes of feeds that are energy dense, therefore feeds that are usually high in starch. Under these conditions there can be significant passage of partially digested feed starch and fibre to the small intestine. These digesta components interact in a manner that can diminish post-ruminal digestion and absorption. However, in ruminants, the effects of digesta composition on causing physical constraints to digestion and absorption, via increased digesta viscosity have not been given adequate consideration. While elevation of digesta viscosity as a means of reducing rate of nutrient absorption and re-absorption of compounds detrimental to health is a valid approach in humans, digesta viscosity may reduce the production potential of livestock. Thus, there is merit in reviewing the feeding strategies that lead to increased viscosity and how its mitigation can be managed through inclusion of oil in ruminant diets. The interactions between digesta composition and the resultant viscosity with digestive physiology and production factors are examined. This review also highlights the associated benefits of dietary oil for ruminants, such as enhancement of bio-active lipids in ruminant products, health benefits directly to the animal, through decreasing incidence of liver abscesses and effects on production and possibly on the environment (reduction of enteropathogenic bacterial shedding and methane emissions). Key words: Ruminant, starch, digesta characteristics, viscosity, cholecystokinin
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".