Microbial adaptations and biogeochemical cycling of uranium in polymetallic tailings
Bibliographic record
Abstract
Microorganisms inhabiting uranium (U)-rich environments have specific physiological and biochemical coping mechanisms to deal with U toxicity, and thereby play a crucial role in the U biogeochemical cycling as well as associated heavy metals. We investigated the diversity and functional capabilities of indigenous bacterial communities inhabiting historic U- and Rare-Earth-Elements-rich polymetallic tailings from the Mount Painter Inlier, Northern Flinders Ranges, South Australia. Bacterial diversity profiling identified Actinobacteria as the predominant phylum in all samples. GeoChip analyses revealed the presence of diverse functional genes associated with biogenic element cycling, metal homeostasis/resistance, stress response, and secondary metabolism. The high abundance of metal-resistance and stress-tolerance genes indicates the adaptation of bacterial communities to the "harsh" environmental (metal-rich and semi-arid) conditions of the Northern Flinders Ranges. Additionally, a viable bacterial consortium was enriched from polymetallic tailings. Laboratory experiments demonstrated that the consortium scrubbed uranyl from solution by precipitating a uranyl phosphate biomineral (chernikovite), thus contributing to U biogeochemical cycling. These specialised microbial communities reflect the high specificity of the mineralogy/geochemistry, and biogeography of these U-rich settings. This study provides the fundamental knowledge to develop future applications in securing long-term stability of polymetallic mine waste, and for reprocessing this "waste" to further extract critical minerals.
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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) | 0.000 | 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 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".