Bor Mine Tailings Treatment
Bibliographic record
Abstract
This work contains the results from experimental testing on the process of oxidation copper leaching under pressure from concentrate obtained after the re-flotation process of tailings. Flotation tailings are a significant resource for recovery of copper and other useful components, since their content in tailings is approximate to the content in the primary raw materials. On the other hand, the dumped flotation tailings, under the influence of the atmosphere, pollute the surrounding land, surface waterways, as well as the groundwater, and present a serious environmental problem. Larger global companies are involved in research related to the process of obtaining the useful components from tailings. Testing the flotation process at the site Katanga [1] was focused on valorization of copper and cobalt from tailings. In the Musselwhite Mine in Ontario, the process of flotation concentration [2,3] has confirmed that this is an effective method for reducing the content of sulphides present in tailings. The effect of sulfuric acid concentration as a leaching reagent was tested, as well as the effect of temperature, pressure, and pulp density on the degree of copper leaching. The obtained results indicate that the use of combined procedure of re-flotation tailings and copper leaching under pressure achieve a high degree of copper separation of over 98%.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".