Chemical composition of rumen microbial fraction and fermentation parameters as affected by tannins and saponins using an in vitro rumen fermentation system
Bibliographic record
Abstract
Castro-Montoya, J. M., Makkar, H. P. S. and Becker, K. 2011. Chemical composition of rumen microbial fraction and fermentation parameters as affected by tannins and saponins using an in vitro rumen fermentation system. Can. J. Anim. Sci. 91: 433–448. Post-rumen chemical composition of the microbial fraction is one of the factors that determines the nutrients absorbed and available for maintenance and production of the animal. The hypothesis was that tannins and saponins alter chemical composition of rumen microbes and fermentation parameters in the rumen. Purified quebracho, mimosa, chestnut and sumach tannins; and quillaja and gypsophilla saponins were incubated with 380 mg of substrate (hay:concentrate 70:30 wt/wt) for 24 h in an in vitro gas production system at concentrations from 0.25 to 1.25 mg mL −1 . Saponins increased N and reduced sugar contents of the liquid-associated microbes. The ratio of crude protein to purine bases significantly increased on adding sumach and chestnut tannins and decreased on the addition of quebracho and mimosa tannins. Quebracho, mimosa and chestnut tannins reduced total short-chain fatty acid production. The acetate:propionate ratio decreased for all additives. Results suggest that in vitro (a) depending on the source and the concentration, tannins would have an effect on the nitrogen and sugar contents of the liquid associated microbes, (b) saponins are likely to increase N and reduce sugar contents of rumen liquid associated microbes, and (c) estimation of microbial protein synthesis based on purine bases may lead to under- or over-estimations in the presence of tannins and saponins. In vivo studies are required to validate these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".