Homemade copper-silver plates as an alternative for cleaning mercury-contaminated tailings from artisanal and small-scale gold mining in Colombia
Bibliographic record
Abstract
Over 300,000 artisanal and small-scale gold mining operate in almost all Colombia. Despite the poverty alleviation characteristic of the operations in rural regions, these practices have been affecting the environment and health of operators as well as neighboring communities, mainly due to the misuse of mercury and cyanide. The most impacting activity is when processing centers use cyanide to leach Hg-contaminated tailings. This generates toxic mercury-cyanide complexes that are not frequently removed from effluents, and it is very toxic for aquatic life. A study to remove metallic mercury from tailings before cyanidation was conducted with 300 to 500 kg tailings from 15 different mining sites in Colombia using homemade copper plates covered with an electrolytic layer of silver. The experiments resulted in an average of 63% of Hg removed, and a maximum of 85%. The final Hg concentrations in the tailings reached an average of 27 to 47 ppm (depending on the analytical method) from the original grades of 75 to 125 ppm. Mercury droplets from old tailings, with acid generation, were difficult to trap by the plates. The article gives a detailed description on how to make these homemade plates and set up a zigzag cascade configuration for better results. Additional methods to adsorb or precipitate mercury from the cyanide solutions are necessary and they have been discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".