Study of Leachability of Heavy Metals from Zinc Flotation Plant Tailings Dam Sediments
Bibliographic record
Abstract
The risk of soil, surface, and ground water contamination by toxic metals leached from mineral processing plants' solid waste is a major environmental concern. In this research the most important factors that affect the leachability of toxic heavy metals from the sediment of a tailing dam in a zinc flotation plant in the city of Arak in central Iran have been studied. The leachability of heavy metals such as Zn, Cd, and Pb were examined. Potential leachability, defined as the maximum metal pool that may become available for leaching at a constant pH decreased in the following order: Pb < Zn < Cd. The results indicated that the highest risk of leachability in Pb and Cd were obtained when the pH in the input solution was 5 while for the Zn the highest mobility was at a pH of 7. The experimental results were statistically analyzed using Statistical Package for Social Sciences (SPSS 15) software. The results of SPSS software indicated that the Zn and Cd removal times were the most effective parameters in the leachability of toxic metals from the sediment. The results of the model also indicated that flow rate, acid concentration and time have little effect on the removal of Pb from sediments in the range of experimental data.
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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 | 0.000 | 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) | 0.001 | 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".