Transformational roasting in the treatment of metallurgical wastes
Bibliographic record
Abstract
The development of the concept of transformational roasting, or roasting with the addition of a solid reagent to produce a desirable mineralogical change in the starting material, is discussed. Preliminary results from the transformational roasting of several samples of metallurgical waste, including zinc ferrite residue, electric arc furnace dust and matte electrorefining residue, with Na2CO3 are also presented. This research shows that transformational roasting of these materials with Na2CO3 can effectively increase the solubility of valuable elements, such as Zn, Cu or Ni, produce a differential solubility between valuable and harmful elements (e.g. between Zn and Cr or between S or As and Cu or Ni) by using different leaching reagents, or control the emission of volatile elements during roasting (e.g. S and As). The addition of secondary additives during roasting with Na2CO3, in turn, allows for improved control over the solubility of a major impurity (e.g. Fe).
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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 | 0.001 |
| 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".