Isolation and characterization of heavy-metal-mobilizing bacteria from contaminated soils and their potential in promoting Pb, Cu, and Cd accumulation by <i>Coprinus comatus</i>
Bibliographic record
Abstract
The enhanced effect of heavy-metal-mobilizing bacteria on the uptake of Pb, Cu, and Cd by Coprinus comatus from Pb-, Cu-, and Cd-multicontaminated soil was assessed in this study. Thirteen strains, tolerating 800 mg·L(-1) Pb, 200 mg·L(-1) Cu, and 200 mg·L(-1) Cd simultaneously were selected for heavy-metal-solubilizing experiments in soil. The mobilization of heavy metals depended on the characteristics of bacteria and heavy metals. Correlation analysis demonstrated that for Pb solubilization, the acid-producing ability was the most significant factor, while for Cu and Cd, siderophores played a leading role in this process. Four strains, based on their excellent ability to solubilize heavy metal in soil, were applied in pot experiments. The results showed that all strains can promote the growth of C. comatus and meanwhile help mushroom accumulate more heavy metals (Pb, Cd, and Cu). The maximum uptake for total Pb and Cu by C. comatus was observed in inoculations with Bacillus sp. strain JSG1 (2.02- and 2.13-fold, respectively, compared with uninoculated soil), while for Cd, it was recorded in Bacillus sp. strain PB2 treated soil (2.03-fold). Therefore, this work suggests that the mushroom-bacteria interaction can be developed into a novel bioremediation strategy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".