Biogeochemical activity of microbial biofilms in the water column overlying uranium mine tailings
Bibliographic record
Abstract
AIMS: To describe microbial diversity, biofilm composition and biogeochemical potential within biofilms in the water overlying uranium tailings characterized by high pH, high metal concentration and low permeability. METHODS AND RESULTS: To estimate microbial diversity in biofilms formed in water columns overlying uranium mine tailings, culture-dependent and culture-independent methods were employed. High-throughput sequencing revealed the presence of 11 phyla; however, the majority of the sequences were affiliated with four major lineages (Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes) as confirmed by culture-based methods. Dominant phylotypes were closely related to methylotrophs (Methylobacterium) and bacterial groups able to utilize complex hydrocarbons (Aquabacterium and Dechloromonas). Microbial diversity in biofilms from the 13 m depth was significantly different that in biofilms from 1 to 41 m (P < 0·05). Phylotypes closely related to iron-reducing bacteria were identified at each depth; whereas sulphate-, thio-sulphate-, sulphite- and sulphur-reducing bacteria, at low abundance, were only detected at lower depths. Confocal scanning laser microscopy (CSLM) was used to investigate polymer quantity and composition of the biofilm components, and principal component analysis of the CLSM data revealed that the relative abundance of α-L-fucose and N-acetyl-glucosamine/lipopolysaccharide residues separated tailings-water interface biofilms from those from other depths. Reduced (ferrous) iron was detected within all the biofilm samples examined by scanning X-ray transmission microscopy. CONCLUSIONS: Microbial communities within the water column covering a highly alkaline uranium tailings body form biofilms with microenvironments where iron reduction takes place. SIGNIFICANCE AND IMPACT OF THE STUDY: This study demonstrates the biogeochemical potential of microbial biofilm communities in the water column covering an alkaline uranium tailings body; specifically, the nature of the bacterial groups detected (Aquabacterium, Dechloromonas) and the presence of reduced iron suggest that complex hydrocarbons are available for bacterial growth and geochemical change, such as iron reduction, can occur even though the system bulk phase is predominantly oxic.
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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 | 0.001 | 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".