Comparative efficiency of various fats rich in medium-chain fatty acids to suppress ruminal methanogenesis as measured with RUSITEC
Bibliographic record
Abstract
A RUSITEC apparatus equipped with eight fermenters was used to investigate the effects of seven different fats with high proportions of medium-chain fatty acids (C 8:0 –C 16:0 ) supplied at a level of 53 g kg −1 DM. In detail, coconut oil, palm kernel oil, palm oil, tallow, milk fat and two types of canola oils, both genetically enriched with lauric acid, were compared with a diet supplemented with prilled fat. The prilled fat was selected from three different rumen-protected fats in a preliminary experiment as the least effective one regarding methane release. Palm kernel oil, coconut oil and one of the canola oils significantly (P < 0.05) decreased methane release, methanogens and ciliates whereas the other fats had only minor effects. With the use of the three effective fats, NDF degradation was also suppressed (P < 0.05), but the rumen fluid concentration of volatile fatty acids remained unchanged. However, at constant acetate proportion, butyrate proportion increased (P < 0.05) at the expense of propionate. Apart from a high proportion of lauric and myristic fatty acids in the fatty acid composition, complete melting at rumen fluid temperature seems to support the ability of some fats to reduce methanogenesis and to suppress methanogens and ciliates. Key words: Methane, fats, medium-chain fatty acids, laurate canola, fermentation, ruminants
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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".