The Production of Soluble Ferric Sulfate via Biological and Chemical Processing of Iron Sulfides
Bibliographic record
Abstract
Ferric sulfate is a very useful reagent for mineral leaching and metal recovery. Ferric sulfate may be used as an oxidative leaching agent for uranium, zinc, copper, nickel and other ores. Ferric ion is a modestly strong oxidant. Similarly, the use of ferric co-precipitation to stabilize arsenic, selenium and other species is in wide use. The demand for ferric sulfate for this application is increasing. Pyrite and pyrrhotite represent minerals that are widely available as sources of soluble iron to provide ferric sulfate for leaching and for iron co-precipitation. The use of biological processes for oxidation of pyrite is well established. However, the common goal is to use biological oxidation to liberate a valuable material (eg. Gold locked in arsenopyrite or pyrite). Much less attention has been paid to production of soluble iron for leaching of other minerals or for use as a precipitant. The use of chemical processes such as atmospheric and pressure oxidation may also be used to generate ferric sulfate from iron sulfide minerals. In this paper the use of biological and chemical processing for production of ferric sulfate will be reviewed and 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 | 0.000 | 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) | 0.001 | 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".