Microbial Inoculants Modify the Functions of Resident Soil Microbes to Expedite the Field Restoration of the Abandoned Mine
Bibliographic record
Abstract
ABSTRACT Global‐scale mining activities have had significant deleterious impacts on local ecosystems and the overall environment, which will necessitate robust restoration efforts. A practical approach includes combining microbial inoculants with the technology of external soil spray seeding. This approach holds the potential for sustainable abandoned mine site restoration by enhancing plant growth through the modulation of soil nutrients and microbial communities. Nonetheless, the detailed effects of microbial inoculants on specific aspects of soil microbial community functions and their complex interactions with plant growth remain underexplored, particularly in the context of restoration efforts. To bridge this gap, we performed a four‐year field study at an abandoned carbonate mine location, using metagenomic sequencing to evaluate the influence of microbial inoculants on soil microbial functionality. Our research revealed that introducing microbial inoculants greatly enhanced essential soil parameters and notably increased plant biomass. Additionally, these inoculants altered the functional gene makeup of the microbial community, significantly boosting the relative abundance of processes such as nitrogen fixation, nitrification, denitrification, assimilatory nitrate reduction (ANRA), dissimilatory nitrate reduction (DNRA), and organic phosphorus mineralization. Conversely, there was a decrease in the relative abundance of carbon degradation, phosphorus regulation, and transport processes. We observed strong correlations between the abundance of nitrogen and phosphorus cycles and plant biomass. Crucially, microbial inoculants affect plant biomass by initially altering soil properties and subsequently coordinating nitrogen and phosphorus cycles. These findings provide valuable insights into the role of microbial inoculants in mine site restoration and offer a theoretical foundation for their broader practical application.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".