Incurred environmental risks and potential contamination sources in an abandoned mine site
Bibliographic record
Abstract
The mineralogical characterization of Fedj Lahdoum mine wastes measured by X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) showed the presence of the following sulfide minerals: galena (PbS), sphalerite (ZnS), pyrite (FeS2), cerussite (PbCO3) and smithsonite (ZnCO3). The wastes were stored in tailing ponds. The results showed that the concentration of metals from tailings were up to 10 460 mg.kg-1 for total Zn, 2 100 mg.kg-1 for total Pb and 62.08 mg.kg-1 for total Cd. The tailings have presented a fine unconsolidated texture that accelerated the dispersion of the particles rich in heavy metals. Geochemical analysis of soil has revealed high total contents of Pb, Zn and Cd, respectively: 3 646, 3 236 and 17 mg.kg-1. Chemical analysis of cultivated and wild plants species inside the district contain high grades in heavy metals: 708.56 mg Zn. kg-1; 16.24 mg Pb.kg-1 (Thymus vulgaris (L)); 500.44 mg Zn. kg-1, 12.44 mg Pb. kg-1 (Laurus nobilis (L)); 128.33 mg Zn. kg-1 and 22.53 mg Pb.kg-1 (Ficus (L)) and 106.73 mgZn.kg-1 (pimento). The high levels detected in soil and plants have exceeded the Tunisian and Canadian standards. These results showed that the abandoned site was contaminated by the presence of tailing dumps which were exposed to significant water and/or wind erosion. To solve this problem, we proposed an environmental desulphurization by froth flotation. Key words: Heavy metals, mine tailings, abandoned mining-district, plant contamination
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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 | 0.001 |
| Research integrity | 0.001 | 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".