Enhanced Electrokinetic Remediation of Mercury-Contaminated Tailing Dam Sediments
Bibliographic record
Abstract
Abstract This study evaluates the use of different extracting solutions at the cathode during electrokinetic remediation to optimize the removal of mercury from gold mine tailing dam sediments in Iran. The total mercury concentration of the soil was 210 mg/kg and the duration of this experiment was 4 weeks. Experiments were conducted on the mine tailing recovered sediments with two voltage gradients (1.0 VDC/cm and 1.5 VDC/cm) to assess the effect of the voltage gradient when employing 0.1M Na-EDTA, 0.1M, and 0.4M KI solutions and distilled water. The test conducted on the soil showed that when the 0.1M and 0.4M KI concentrations were employed with a voltage gradient of 1.0 VDC/cm, approximately 50 % and 70 %, respectively, of the mercury was removed from the sediment. Also, it is understood that when the 0.1M and 0.4M KI concentrations were used with a voltage gradient of 1.5 VDC/cm, 65 % and 56 %, respectively, of the mercury was removed from the contaminated soil. The tests showed that mercury removal from sediment was less with distilled water and Na-EDTA as the extracting agents. The results also indicated that electrokinetic remediation for the concentration of 0.4M KI and with a voltage gradient of 1.0 VDC/cm was optimal for the approximately 70 % removal of the initial contamination. The reason for the remaining mercury in the sediment could be the presence of CaO, other metals, and organic compounds.
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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".