Effects of Phytoremediation on Microbial Biomass, Composition, and Function in a Sulphide-Rich Tailing From a Metal-Contaminated Region
Bibliographic record
Abstract
Mining activities lead to serious land deterioration and large scale mine waste generation. Reclamation has been carried out on several technogenic materials to encourage the development of soils. To date no detailed studies have been conducted to assess if soil developed in reclaimed tailings can be suitable for microbial community sustainability and associated plant population. This study investigated if 1) soil metal contamination affects microbial biomass and composition in sulphide tailings and 2) phytoremediation of tailing increases microbial abundance, diversity, and function. Microbial biomass was assessed using Phospholipid fatty acid analysis (PLFA). Soil bacterial and fungal microbiota was determined by high throughput sequencing of 16S rRNA gene for bacteria and internal transcribed spacer region for fungi using the Illumina platform. Total copper, nickel, iron, and titium were higher in unreclaimed sites compared to vegetated areas but the total microbial biomass was significantly higher in reclaimed sites compared to reference areas. More importantly, the levels of microbial biomass were not impacted by metals since the bioavailable Cu, Ni, and Ti were low in all the sites. Site-specific bacterial and fungal genera were identified. Proteobacteria was the most dominant bacterial phylum while Ascomicota was the predominant fungal phylum. Interestinlgy, Acidiferrobacter, an acidophilic, thermotolerant and facultatively anaerobic was the most predominant genus in unreclaimed site that is characterized by extreme acidity (pH = 2.8). Analysis of microbial diversity revealed higher Chao 1, # of OTUs, Shannon index, and species richness in bacterial and fungal populations from reclaimed sites compared to controls. The levels of β-glucosidase (BG), cellobiohydrolase (CBH), β-N-acetylglucosaminidase (NAGase), aryl sulfatase (AS), acid phosphatase (AP), alkaline phosphatase (AlP), glycine aminopeptidase (GAP), and leucine aminopeptidase (LAP) activities were significantly higher in vegetated sites compared to reference areas. Strong positive correlation coefficients were observed between soil organic matter and total microbial biomass (r = 0.99). These two factors were positively correlated with enzymatic activities and bacterial population diversity. Overall, newly developed soils can sustain diverse microbial communities and associated vegetations.
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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) | 0.001 | 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".