The use of EDTA leaching method to predict arsenic and antimony Neutral Mine Drainage from the Eleonore tailings
Bibliographic record
Abstract
The prediction of neutral mine drainage (NMD) is difficult using classical kinetic techniques due to the sorption and precipitation processes that retain the contaminant within the material, hiding the actual geochemical behaviour. A method for NMD prediction using sorption experiments and modified kinetic experiments with a complexing agent such as ethylenediaminetetraacetic acid (EDTA) was developed to predict metal leaching in mine waste. The objective of this study was to assess the applicability of the leaching procedure to oxyanions such as As and Sb as well as the evolution of the risk of Eleonore mine tailings towards As and Sb leaching in the long-term. The study found that the Eleonore tailings contained 527 mg/kg of As and 59 mg/kg of Sb, mostly found within löllingite and arsenopyrite. The leaching of As and Sb through complexation with EDTA was found to be effective in kinetic experiments for prediction purposes, despite EDTA's classification as a cation complexing agent. The tailings sorption capacity for As was estimated to be between 43 and 76 mg/kg. By comparing the sorption capacity and the metalloid content, the ratio sorption/metalloid content was found to be below 1 (0.07–0.13), indicating a high risk towards NMD. Furthermore, no notable change in sorption capacity was observed over the course of the column experiments, suggesting that sorption is unlikely to influence the As leaching dynamic. However, it is probable that As retention in the tailings is not primarily driven by sorption; given the high iron (Fe) loadings, coprecipitation may be the dominant mechanism. • CND prediction methodology was applied to desulfurized tailings from Eleonore mine • Characterizations and field data suggest main elements of concern are As and Sb • EDTA kinetic tests confirmed the leachability of the As and Sb • Coprecipitation of As+Sb with Fe is probable and controls leachates concentrations • Concentration will remain stable with coprecipitation and risk unlikely to worsen
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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 | 0.001 | 0.001 |
| 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".