Technogenic Deposits in Russia: Precious Metals Stocks and Prospects of Their Recovery
Bibliographic record
Abstract
Russia possesses a high potential for resource growth and platinoid reserves, and technogenic waste of complex ore processing can become a considerable part of the process in the near future. It is practical to take into account impounded mill tailings of sulphide copper-nickel ores, old pyrrhotine concentrates (OPC), impounded magnetite concentrates and slag-dust dumps of the mining and metallurgical company “Norilsk Nickel” (MMC NN), as well as technogenic platinum-metal chromite placer deposits of Ural and Aldan. In spite of big volumes, secondary resources are characterized by unstable content of PGM and nonferrous metals. The forms of finding platinum group metals are so that raw material is difficult to be processed by conventional technological schemes. At that production cost of PGM extraction from technogenic deposits sometimes can be lower than when concentration of initial ores and sands, whereas the cost intensive operations connected with mining, crushing, grinding and classification are excluded from the processing chain.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".