Using probiotics to improve swine gut health and nutrient utilization
Bibliographic record
Abstract
To maintain a healthy gut is definitely key for a pig to digest and absorb dietary nutrients efficiently. A balanced microbiota (i.e., a healthy micro-ecosystem) is an indispensable constituent of a healthy gut. Probiotics, the live microorganisms which, when administered in adequate amounts, confer good health benefits onto the host, are a category of feed additives that can be used to replenish the gut microbial population while recuperating the host immune system. Besides their antitoxin and diarrhea reduction effects, dietary supplementation of probiotics can improve gut health, nutrient digestibilities and, therefore, benefit nutrient utilization and growth performance of pigs. Current knowledge in the literature pertinent to the beneficial effects of utilizing various probiotics for swine production has been comprehensively reviewed, and the safety and the risk issues related to probiotic usage have also been discussed in this paper. Considering that the foremost cost in a swine operation is feed cost, feed efficiency holds a very special, if not the paramount, significance in commercial swine production. Globally, the swine industry along with other animal industries is moving towards restricting and eventually a total ban on the usage of antibiotic growth promoters. Therefore, selection of an ideal alternative to the in-feed antibiotics to compensate for the lost benefits due to the ban on the antibiotic usage is urgently needed to support the industry for profitable and sustainable swine production. As is understood, a decision on this selection is not easy to make. Thus, this review paper aims to provide some much needed up-to-date knowledge and comprehensive references for swine nutritionists and producers to refer to before making prudent decisions and for scientists and researchers to develop better commercial products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".