Effect of silver on gold cyanidation in mixed and segregated sulphidic minerals
Bibliographic record
Abstract
Abstract Gold is mostly found in nature in metallic form and is associated with sulphide minerals and other precious metals, most often silver. Pyrite, chalcopyrite, sphalerite, and stibnite are the sulphidic minerals investigated in the present gold‐silver cyanidation study by adopting the metal‐sulphide multi‐layer packed‐bed reactor approach. Gold leaching kinetics was enhanced remarkably for the pyrite and chalcopyrite sulphide minerals, with 94.5 % and 85 % recovery respectively. The influence of a bilayer sulphidic mineral system on the dissolution of gold was also investigated with a maximum gold dissolution of 87 % for the pyrite‐chalcopyrite bilayer system. The effect of silver on the dissolution of gold was investigated with the gold associated with sulphidic minerals in an arrangement of mixed as well as segregated mineral layers. Three sets of mineral systems were established: pyrite‐chalcopyrite‐silica, pyrite‐sphalerite‐silica, and pyrite‐stibnite‐silica systems. The addition of silver enhanced the gold dissolution for the pyrite‐sphalerite‐silica system, slightly lessened the gold dissolution with the pyrite‐stibnite‐silica system, and retarded gold dissolution severely in the case of the pyrite‐chalcopyrite‐silica system, irrespective of the dispersion of gold and silver in the same as well as in the segregated mineral layers.
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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 | 0.000 | 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) | 0.000 | 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".