Microbial communities in low permeability, high pH uranium mine tailings: characterization and potential effects
Bibliographic record
Abstract
AIMS: To describe the diversity and metabolic potential of microbial communities in uranium mine tailings characterized by high pH, high metal concentration and low permeability. METHODS AND RESULTS: To assess microbial diversity and their potential to influence the geochemistry of uranium mine tailings using aerobic and anaerobic culture-based methods, in conjunction with next generation sequencing and clone library sequencing targeting two universal bacterial markers (the 16S rRNA and cpn60 genes). Growth assays revealed that 69% of the 59 distinct culturable isolates evaluated were multiple-metal resistant, with 15% exhibiting dual-metal hypertolerance. There was a moderately positive correlation coefficient (R = 0·43, P < 0·05) between multiple-metal resistance of the isolates and their enzyme expression profile. Of the isolates tested, 17 reduced amorphous iron, 22 reduced molybdate and seven oxidized arsenite. Based on next generation sequencing, tailings depth was shown to influence bacterial community composition, with the difference in the microbial diversity of the upper (0-20 m) and middle (20-40 m) tailings zones being highly significant (P < 0·01) from the lower zone (40-60 m) and the difference in diversity of the upper and middle tailings zone being significant (P < 0·05). Phylotypes closely related to well-known sulfate-reducing and iron-reducing bacteria were identified with low abundance, yet relatively high diversity. CONCLUSIONS: The presence of a population of metabolically-diverse, metal-resistant micro-organisms within the tailings environment, along with their demonstrated capacity for transforming metal elements, suggests that these organisms have the potential to influence the long-term geochemistry of the tailings. SIGNIFICANCE AND IMPACT OF THE STUDY: This study is the first investigation of the diversity and functional potential of micro-organisms present in low permeability, high pH uranium mine tailings.
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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".