Microbial Inoculants Drive Changes in Soil and Plant Microbiomes and Improve Plant Functions in Abandoned Mine Restoration
Bibliographic record
Abstract
The application of microbial inoculants holds promise for the sustainable restoration of abandoned mine sites by affecting soil nutrients and microbial communities. However, the responses of plant microbial communities to microbial inoculants in mine restoration remain largely unknown. To bridge this knowledge gap, we conducted a 4-year field experiment at an abandoned carbonate mine site to assess the impacts of microbial inoculants on the soil-plant microbiome. Our findings revealed that microbial inoculants significantly changed roots, fine root bacterial and fungal communities. Further, no significant correlations were observed between the soil-plant nutrient content (Z-score) and microbial alpha diversity. However, a significantly positive correlation was found between the relative abundance of the keystone ecological cluster (Module #1) and soil-plant nutrient content. The application of microbial inoculants also increased complexity, albeit decreased stability of plant microbiome networks, alongside a reduction in stochastic assembly. Conversely, they decreased the complexity but increased the stability of soil microbiome networks, accompanied by an increase in stochastic assembly. Notably, the number of specifically enriched microbiome functional traits of roots and root nodules under the microbial inoculant treatments surpassed that of the control. In summary, our findings underscored the potential of microbial inoculants to enhance soil-plant functionality at abandoned mine restoration sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".