Heavy metal concentration in soil in the tailing dam vicinity of an old gold mine in Johannesburg, South Africa
Bibliographic record
Abstract
Soil contamination with heavy metals is a serious concern to food production and human health. The present study was conducted to evaluate the impact of tailings from an old mining site on heavy metal contamination of soil. Using a GPS to map out different sites around the tailing dam, soil samples were taken from under grassland at the different sites, at depths of 15 cm and 1 m, using a shovel and handheld auger. The samples were prepared, acid digested, and analyzed for a multi-element suite by inductively coupled plasma atomic emission spectrometry. Results showed heavy metal concentration in the order of Cr > Zn > As > Mn > Cu > Pb > Ni > Sr > Hg. Most of the soil samples contained high concentration of As (13.46–234.6 mg kg−1). Soil concentrations of As, Hg, Cr, and Mn also decreased with distance from the dump material. Single contamination index of each pollutant, calculated according to the South African Soil Quality Standards revealed very high and medium pollution grades for As (index = 7.39) and Cr (index = 2.16), respectively. Arsenic is a metal associated with gold ore and soil pollution by such metals can make it infertile and unsuitable for plants.
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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 | 0.000 | 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 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".