New insights into the composition and diversity of endophytic bacteria in cultivated <i>Huperzia serrata</i>
Bibliographic record
Abstract
Endophytic bacteria play crucial roles in the growth and bioactive compound synthesis of host plants. In this study, the composition and diversity of endophytic bacteria in the roots, stems, and leaves from 3-year-old artificially cultivated Huperzia serrata were investigated using Illumina HiSeq sequencing technology. Total effective reads were assigned to 936 operational taxonomic units (OTUs), belonging to 12 phyla and 289 genera. A total of 28, 3, and 2 OTUs were exclusive to the roots, stems, and leaves, respectively. The bacterial richness and diversity in the roots were significantly lower than those in the leaves and stems. The dominant genera with significant distribution differences among these plant tissue samples were Burkholderia-Caballeronia-Paraburkholderia, Sphingomonas, Acidibacter, Bradyrhizobium, Bryobacter, Methylocella, Nocardioides, Acidothermus, and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium. Furthermore, the differences in the bacterial communities associated with these plant tissue samples were visualized using principal coordinate analysis and cluster pedigree diagrams. Linear discriminant analysis effect size explained statistically significant differences among the endophytic bacterial microbiota in these plant tissue samples. Overall, this study provides new insights into the diversity and distribution patterns of endophytic bacteria in the different tissues of H. serrata.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".