Responses of microbial communities during oilseed plant-based phytoremediation of heavy metal-contaminated soils
Bibliographic record
Abstract
AIMS: Phytoremediation is an effective method of remediating soils contaminated with heavy metals. However, it has some limitations in practical applications with regard to rare plant species, poor environmental adaptability, and long growth cycles. The dynamic response mechanisms of soil microbial communities during phytoremediation are still unclear, which restricts the optimization and promotion of this approach. METHODS AND RESULTS: No ethical approval was required for this study. In this study, soil bacterial, fungal, and archaeal communities during the remediation of Cu-, Pb-, and Zn-contaminated soils with five industrial oilseed plants [Xanthium strumarium (XS), Bidens pilosa (BP), Kosteletzkya virginica (KV), Sesbania cannabina (SC), and Commelina communis (CC)] were analyzed using metagenome sequencing. Compared with soil contaminated with heavy metals, remediation through five industrial oilseed plants significantly reduced the content of heavy metals in the soil, with soil Cu, Pb, and Zn decreasing by 44.01%, 46.32%, and 27.62%, respectively, and WSCu, WSPb, and WSZn content decreasing by 28.23%, 50.68%, and 75.26%, respectively. Microbial diversity analysis showed that the phytoremediation significantly affected the soil microbial communities, with a significant decrease in archaeal diversity. Variation partitioning analysis and Mantel tests revealed that heavy metals and soil physicochemical properties significantly affected microbial communities, and heavy metals exerted stronger effects on archaeal communities. Meanwhile, soil contaminated with heavy metals was mainly dominated by fungal-fungal interactions, whereas phytoremediation increased the complexity of microbial symbiotic networks. CONCLUSION: Collectively, these results provide fundamental insights into the microbial community structure during phytoremediation of heavy metal-contaminated soil, which may aid in the bioregulation of phytoremediation.
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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".