Effect of bamboo vinegar powder as an antibiotic alternative on the digesta bacteria communities of finishing pigs
Bibliographic record
Abstract
This study investigated the use for bamboo vinegar powder as an antibiotic alternative in the diet of growing-finishing pigs by examining their digestive bacterial communities. Forty-five Duroc × Landrace × Yorkshire growing-finishing pigs were randomly allocated to five diet groups: 0%, 0.5%, 1.0%, or 1.5% bamboo vinegar levels and antibiotics. After 37 days, the digesta in duodenum of four pigs from each treatment were analyzed for their bacterial community compositions using 16S rRNA gene sequencing. The addition of 1.5% bamboo vinegar powder had an effect on the intestinal microflora most similar to that of antibiotics, indicating its potential to promote the growth and development of finishing pigs. We also found the 1.5% bamboo vinegar powder group to have an increased abundance of Firmicutes/Bacteroidetes compared with the other bamboo vinegar powder groups, which may enhance the ability of the host to absorb food energy and store more body fat. Additionally, the effects of bamboo vinegar powder on promoting the abundances of Lactobacillus and Thalassospira and on inhibiting Streptococcus and Prevotella growth revealed it may play an important role in animal production. Moreover, functional predictions of microbes via PICRUSt indicated that feed supplemented with 1.5% bamboo vinegar powder could promote many vital metabolic pathways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".