Effects of several in-feed antibiotic combinations on the abundance and diversity of fecal microbes in weaned pigs
Bibliographic record
Abstract
To characterize the effects of several subtherapeutic antibiotic combinations on the abundance and diversity of fecal microbes, 400 weaned pigs were selected and randomly assigned to 8 groups, where they were continuously fed different antibiotic combinations for 28 days. Then, a total of 48 pigs were randomly selected to collect feces samples for DNA extraction and 16S rRNA high-throughput sequencing. Compared with that of pigs without antibiotic administration, the diversities of fecal microbes were decreased in the pigs fed chlortetracycline (CTC), olaquindox (OLA), and either enramycin (ER) or virginiamycin (VIR), and the relative abundances of members of the phylum Bacteroidetes and the genus Prevotella were increased. Compared with that of pigs without antibiotic administration, the relative abundances of members of the phylum Proteobacteria and the genus Succinivibrio were decreased in the pigs fed CTC, a mixture of zinc bacitracin (ZB) + colistin sulfate (COL), a mixture of CTC+ZB+COL, a mixture of CTC+OLA, or a mixture of CTC+OLA+ER. α-Diversity and β-diversity were decreased (P < 0.05) in the pigs fed ZB+COL or a mixture of CTC+OLA+ER or CTC+OLA+VIR. This study provides experimental data to deepen our understanding on the effects of antibiotic combinations on intestinal microbes.
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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) | 0.001 | 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 | 0.001 |
| 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".