Review: Chinese herbs as alternatives to antibiotics in feed for swine and poultry production: Potential and challenges in application
Bibliographic record
Abstract
Gong, J., Yin, F., Hou, Y. and Yin, Y. 2014. Chinese herbs as alternatives to antibiotics in feed for swine and poultry production: Potential and challenges in application. Can. J. Anim. Sci. 94: 223–241. Traditional Chinese medicine (TCM) has a long history of clinical practice, and its own theoretical framework focused on functions at the whole-body level. However, due to cultural differences, TCM has not been fully recognized in Western countries. With the recent development of the theory of whole-body systems biology and “-omics”, there is a new opportunity to study TCM and to close the gap between TCM and Western medicine, because of the similarity in the theoretical foundations between TCM and whole-body systems biology. The uniqueness of TCM theory and practice is the approach to maintain and restore the body balance as a whole with no or little unfavourable side effects. Recent studies have also shown that Chinese herbs used as feed additives can modulate nutritional metabolism, immune responses, and intestinal health of food-producing animals, demonstrating good potential as substitutes for dietary antibiotics. Nonetheless, some issues need to be addressed before Chinese herbs can reach their full application. This article has critically reviewed recent progresses in scientific research of Chinese herbs as feed additives and their potential to replace dietary antibiotics. Possible challenges in future application for swine and poultry production are also discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".