The impact of the endophytic bacterial community on mulberry tree growth in the Three Gorges Reservoir ecosystem, China
Bibliographic record
Abstract
Plant-associated microbes influence plant performance and may also impact biotic and abiotic stress tolerance. The microbiome of mulberry trees planted for ecological restoration in the hydro-fluctuation belt of the Three Gorges Reservoir Region, China, exhibited distinct patterns of localization. The endosphere exhibited lower α-diversity relative to the rhizosphere, but was more closely related to host growth status, especially in stem tissues. Pantoea was the predominant bacterial genus inhabiting the stems of two well-growing plants, while sequences identified as Pseudomonas and Pantoea were abundant in poorly growing plants. The complexity of the endophytic community was more connected to growth status in well-growing plants than it was in poorly growing plants. Among 151 endophytes cultured from collected samples of mulberry, 64 exhibited plant growth-promoting (PGP) potential in vitro and the majority of beneficial taxa were harvested from well-growing plants. Collectively, the present study indicates that the recruitment of beneficial endophytes may contribute to mulberry fitness under abiotic stress, and it provides a foundation for the development of a new strategy in vegetation restoration.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".