Tannin Supplementation in Animal Feeding; Mitigation Strategies to Overcome the Toxic Effects of Tannins on Animal Health: A Review
Bibliographic record
Abstract
Optimum animal production depends on the type of feedstuffs available for the whole year. Plants such as trees and shrubs have significance in the animal production due to high nutritious values of tannins which reduce the rumen protein degradability and improve the feed digestibility as well as amino acid absorption in the small intestine. But, some of these plant materials are usually rich in anti-nutritional factors, known as tannins. Tannins are a complex group of plant secondary compounds widely distributed in the plant kingdom. Tannins have both deleterious and advantageous effects depending on the types, doses, composition and concentration of tannins. Rural farmers face fodder scarcity during the winter season. Therefore, tannins containing browse species may help farmers to use tannins as supplementary feed for livestock. But higher concentrations of tannins cause metabolic disorder which in results reduces the animal productivity. Therefore, it is need to investigate the effects of tannin supplementation in animal feeding; mitigation strategies to overcome the toxic effects of tannins on animal health. In this review paper, we have discussed safety and hazard associated with tannins in animal feeding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".