Simulation of cyanide oxidation using calcium and sodium hypochlorite in the Moteh Gold Mine Tailing Dam, Iran
Bibliographic record
Abstract
ABSTRACT Cyanide, as one of the most toxic pollutants existing in the gold mine tailing dams, threatens human health and other species life. The main objective of this study was to simulate the oxidation of cyanide from mineral effluent of the Moteh Tailing Dam (Iran), as a method for removing cyanide. We employed PHREEQC software to model the oxidation of cyanide using calcium hypochlorite (Ca(OCl) 2 ) and sodium hypochlorite (NaOCl). The results indicated that Ca(OCl) 2 and NaOCl concentrations, as well as pH, influenced the oxidation of cyanide. The model was run in the constant temperature of 12°C and pH between 12 to 13. By rising Ca(OCl) 2 concentration from 0.71 to 1.43 g/l and NaOCl concentration from 1.72 to 5.18 g/l, the removal rates of cyanide increased from 97.01% to 99.20% and 95.46% to 95.90%, respectively. The coefficient of determination (R 2 ), index of agreement (IA), and Nash- Sutcliffe efficiency (E) were used to assess the predicted removal rate of cyanide in comparison with experimental observations, which demonstrated a suitable agreement: Ca(OCl) 2 = 0.71 mg/l, R 2 = 0.97, IA = 0.91 and E = 0.73; Ca(OCl) 2 = 0.85 mg/l, R 2 = 0.99, IA = 0.99, E = 0.96; Ca(OCl) 2 = 1.43 mg/l, R 2 = 0.97, IA = 0.92, E = 0.79; NaOCl = 1.72 mg/l, R 2 = 0.97, IA = 0.92, E = 0.74; NaOCl = 3.45 mg/l, R 2 = 0.91, IA = 0.91, E = 0.71; NaOCl = 5.18 mg/l, R 2 = 0.96, IA = 0.91, E = 0.77.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".