Investigation of techniques to replace amalgamation in artisanal gold mining operations
Bibliographic record
Abstract
Artisanal gold mining is the leading source of anthropogenic mercury emissions worldwide. This article explores cost-effective gold leaching methods for skilled miners to address mercury's environmental and health impacts in this sector. A sample from Colombia's La Maria deposit, containing 48.87 ppm gold, ground to 80% below 0.13 mm, served as the basis for comparison. The standard whole ore amalgamation approach, commonly used by miners, resulted in less than 19% gold extraction. However, employing cyanidation with 1g/L of free cyanide extracted 84% of the gold within 24 hours. Among the unconventional processes investigated, using effluent from a local cassava processing plant as a lixiviant demonstrated promise. Two cassava varieties produced liquids with 267 and 600 mg/L of free cyanide, leading to 50.9% and 82.4% gold extraction from the ore in 24 hours, respectively. Another alternative method involved employing Dimethyl sulfoxide, a water-free reagent, as a gold-leaching lixiviant, resulting in a remarkable 96.5% gold extraction in just 2 hours. The researchers also modified the Merrill-Crowe cyanidation process by eliminating filtration and vacuum, instead utilizing bags filled with zinc (or aluminum) in the pulp. With reduced agitation to prevent aeration, over 99% of the gold precipitated on the zinc shavings within 2 hours. Furthermore, testing a hydrochloric solution (50%) with an oxidant demonstrated 83.9% and 100% gold extraction in 8 hours at ambient temperature and 50°C, respectively. It is crucial to note that successful implementation of these alternative strategies necessitates knowledge, investment, and customization to suit specific site conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".