Niche specificity and functional diversity of the bacterial communities associated with Ginkgo biloba and Panax quinquefolius
Bibliographic record
Abstract
Plant-associated bacteria can establish mutualistic relationships with plants to support plant health. Plant tissues represent heterogeneous niches with distinct characteristics and may thus host distinct microbial populations. The objectives of this study are to investigate the bacterial communities associated with two medicinally and commercially important plant species; Ginkgo biloba and Panax quinquefolius using high Throughput Sequencing (HTS) of 16S rRNA gene, and to evaluate the extent of heterogeneity in bacterial communities associated with different plant niches. Alpha diversity showed that number of operational taxonomic units (OTUs) varied significantly by tissue type. Beta diversity revealed that the composition of bacterial communities varied between tissue types. In Ginkgo biloba and Panax quinquefolius, 13% and 49% of OTUs, respectively, were ubiquitous in leaf, stem and root. Proteobacteria, Bacteroidetes, Actinobacteria and Acidobacteria were the most abundant phyla in Ginkgo biloba while Proteobacteria, Bacteroidetes, Actinobacteria, Plantomycetes and Acidobacteria were the most abundant phyla in Panax quinquefolius. Functional prediction of these bacterial communities using MicrobiomeAnalyst revealed 5843 and 6251 KEGG orthologs in Ginkgo biloba and Panax quinquefolius, respectively. A number of these KEGG pathways were predicted at significantly different levels between tissues. These findings demonstrate the heterogeneity, niche specificity and functional diversity of plant-associated bacteria.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".