The role of silver minerals on the cyanidation of gold particles embedded within multi‐sulphidic mineral matrices
Bibliographic record
Abstract
Abstract The effect of silver minerals on the dissolution behaviour of gold particles embedded within multi‐sulphidic minerals was investigated. A multi‐layer packed‐bed reactor approach was used to study the leaching behaviour of free gold (within silica) and gold associated with a series of synthetic multi‐mineral systems consisting of pyrite, silica, and successively, X = chalcopyrite, sphalerite, and stibnite. The reactor was filled with sieved powders of sulphidic minerals (pyrite, X), gold and silica and arranged as electrically‐isolated three‐layer //Pyrite//X//Silica// and two‐layer //Pyrite + X//Silica// systems. Gold powder was introduced successively in each layer of the three‐ and two‐layer mineral systems and the gold leaching behaviour was studied. The highest gold recovery was achieved for the gold particles within the pyrite layer while the lowest was within the silica layer. In case of the //Pyrite//Stibnite//Silica// system, the surface passivation inhibited gold leaching strongly. Gold cyanidation experiments were also performed with the addition of silver minerals, with both Au and Ag minerals dispersed in a three‐layer //Pyrite//X//Silica// and two‐layer //Pyrite + X//Silica// systems. Silver minerals addition proved beneficial for the pyrite‐sphalerite‐silica system. Gold leaching was severely retarded for the pyrite‐chalcopyrite‐silica and pyrite‐stibnite‐silica systems. Passivating films were observed at the surface of gold particles in case of gold cyanidation with pyrite‐chalcopyrite‐silica and pyrite‐stibnite‐silica systems.
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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 | 0.000 |
| 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) | 0.000 | 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".