Bacterial diversity and composition of an alkaline uranium mine tailings-water interface
Bibliographic record
Abstract
The microbial diversity and biogeochemical potential associated with a northern Saskatchewan uranium mine water-tailings interface was examined using culture-dependent and -independent techniques. Morphologically-distinct colonies from uranium mine water-tailings and a reference lake (MC) obtained using selective and non-selective media were selected for 16S rRNA gene sequencing and identification, revealing that culturable organisms from the uranium tailings interface were dominated by Firmicutes and Betaproteobacteria; whereas, MC organisms mainly consisted of Bacteroidetes and Gammaproteobacteria. Ion Torrent (IT) 16S rRNA metagenomic analysis carried out on extracted DNA from tailings and MC interfaces demonstrated the dominance of Firmicutes in both of the systems. Overall, the tailings-water interface environment harbored a distinct bacterial community relative to the MC, reflective of the ambient conditions (i.e., total dissolved solids, pH, salinity, conductivity, heavy metals) dominating the uranium tailings system. Significant correlations among the physicochemical data and the major bacterial groups present in the tailings and MC were also observed. Presence of sulfate reducing bacteria demonstrated by culture-dependent analyses and the dominance of Desulfosporosinus spp. indicated by Ion Torrent analyses within the tailings-water interface suggests the existence of anaerobic microenvironments along with the potential for reductive metabolic processes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".