Desulfurization of the Old Tailings at the Au-Ag-Cu Tiouit Mine (Anti-Atlas Morocco)
Bibliographic record
Abstract
Tailings from the abandoned Tiouit mine site in Morocco are mainly composed of sulfides, hematite, and quartz. They contain 0.06–1.50 wt % sulfur, mostly in the form of pyrite, pyrrhotite, and chalcopyrite. The tailings also contain gold (3.36–5.00 ppm), silver (24–37 ppm), and copper (0.06–0.08 wt %). Flotation tests were conducted to reprocess the tailings for Au, Ag, and Cu recovery, and at the same time to prevent acid mine drainage (AMD) generation through the oxidation of sulfide minerals, including pyrite, sphalerite, arsenopyrite, chalcopyrite, galena. The flotation results confirmed that environmental desulfurization is effective at reducing the overall sulfide content in the tailings. The recovery of sulfides was between 69% and 75%, while Au recovery weight-yield was between 2.8% and 4.7%. The test that showed the best sulfur recovery rate and weight-yield was carried out with 100 g/t CuSO4 (sulfide activator) and 50 g/t of amyl xanthate (collector). The goal of this study was also to assess the remaining acid-generating potential (AP) and acid-neutralizing potential (NP) of the desulfurized tailing. The geochemical behavior of the initial tailings sample was compared to that of the desulfurized tailings using kinetic weathering cell tests. The leachates from the desulfurized tailings showed higher pH values than those from the initial tailings, which were clearly acid-generating. The residual acidity produced by the desulfurized tailings was most likely caused by the hydrolysis of Fe-oxyhydroxides.
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 | 0.000 |
| 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 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".