The effect of electro-activation and eggshell powder on the neutralization of acid mine drainage
Bibliographic record
Abstract
Acid mine drainage (AMD) production by sulfide mine tailing (SMT) is a major environmental preoccupation because it can degrade water surface quality on account of its strong acidity and advanced content of sulfide, iron (Fe) and other metals and metalloids. Acid neutralization and the precipitation of metals present in AMD were carried out by electro-activation with ion-exchange membranes, which is based on the self-generation of necessary conditions for acid neutralization and metal precipitation. The treatment of SMT was carried out by using an electro-activation cell generated alkaline solution in the cathode compartment. After 60 min of electro-activation, a pHcatholyte of 7.9–9.6, depending on the experimental conditions, was obtained. The absence of Fe and other trace metal ions in the catholyte provide evidence that the electro-activation of SMT promotes the precipitation of insoluble trace metals in the cathode compartment. This approach can be applied to real conditions in combination with a pretreatment of SMT neutralization, in which biological calcareous amendments are available. Finally, the electro-activation technology of acid mine drainage may be a feasible, cost-effective approach for SMT neutralization because it focuses on sustainable development.
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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".