Physicochemical properties, heavy metals, and metal-tolerant bacteria profiles of abandoned gold mine tailings in Krugersdorp, South Africa
Bibliographic record
Abstract
Mine tailings are a potential source of heavy metals (HM) that can be toxic to microbes, plants, and animals in aquatic and terrestrial ecosystems. Bacteria have evolved several mechanisms to tolerate the uptake of HM ions. This study aimed to assess the physicochemical properties, concentrations of selected HM and metalloids [arsenic (As), nickel (Ni), lead (Pb), zinc (Zn), cadmium (Cd), and cobalt (Co)], and isolate potential metal-tolerant bacteria present at three abandoned gold mining sites with a view of understanding how tailings characteristics vary and the implications on microbial activities in tailings dumps. Heavy-metal-tolerant bacteria were isolated from the samples using minimum inhibitory and maximum tolerable concentrations of the Ni, Pb, Zn, Cd, and Co. The substrates of the studied sites were acidic and deficient in nutrients. High metals and metalloid concentrations in the order Zn > Ni > Co > As > Pb > Cd were recorded in some of the studied sites and its adjacent soil which exceeded South African recommended values for soil and sediments. Heavy-metal-tolerant bacteria that showed multiple tolerances to Ni, Pb, and Zn were isolated and putatively identified using biochemical tests as belonging to the phyla Proteobacteria, Actinobacteria, and Firmicutes. Gold mine tailings enriched the soil with HM and also affect soil physicochemical properties. Proper management of mine wastes must be ensured to prevent their adverse effects on the diversity, composition, and activity of soil microorganisms that help in maintenance of the ecosystem.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".