Endophytic bacterial communities and spatiotemporal variations in cotton roots in Xinjiang, China
Bibliographic record
Abstract
Endogenous bacteria are important for maintaining the health and other ecologically relevant functions of cotton plants. However, little is known about the community structures and diversity of endophytic bacteria in cotton plants. In our study, we used the Illumina amplicon sequencing technology to study the endophytic bacteria found in cotton root tissue in Xinjiang, China. A total of 60.84 × 106 effective sequences of the 16S rRNA gene in the V5–V6 variable region revealed a large number of operational taxonomic units (OTUs), namely 81–338 OTUs, at a cut-off level of 3% and a sequencing depth of 50 000 sequences. Among the 23 classes identified, Gammaproteobacteria was the dominant group, followed by Alphaproteobacteria, Actinobacteria, and Bacillus. The diversity of endogenous bacteria differed at different growth periods, with the most OTUs detected in seedlings (654), followed by the budding stage (381), flowering stage (350), and flocking stage (351). A total of 217 OTUs were common to all four stages. Pantoea tags were more common to the Shihezi region, whereas Erwinia labels were more common to the Hami region. These results suggest that the dynamics of endophytic bacterial communities are affected by plant growth stage. This highlights the relevance of microbial diversity studies in improving our understanding of endophyte communities.
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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 | 0.001 | 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".