Relating to the rational utilization of manganese-containing raw materials
Bibliographic record
Abstract
Data on main manganese ores deposits by Russian Federation subjects presented. It was shown, that main part of manganese ore raw materials prognostic resources are concentrated in Altaj-Sayan and Enisej-East-Sayan metallogenic provinces. Estimation of metallurgical value of manganese ores deposits, located at the territory of Altaj-Sayan metallogenic province, carried out. A technological flow-chart of manganese-containing raw materials elaborated, comprising high quality manganese concentrate obtaining, its preparation, synthesis of marokite and mono-phase CaMnO3 material, marokite briquetting with a reducing agent and application for steel processing in ladle-furnace facility. A possibility shown to utilization of CaMnO3 mono-phase material mixed with a reducing agent and high quality manganese concentrate for production of metal manganese. Thermodynamic calculations and experiment studies on polymetallic manganese-containing raw material beneficiation enabled to determine main technological parameters of extraction and elaborate a technological flow-chart of beneficiation. The elaborated technology enables to obtain high quality manganese, nickel, iron and cobalt concentrates. Application of optimal technological parameters of beneficiation enables to extract from a polymetallic manganese-containing raw materials up to 95–97% of manganese, 98–99% of nickel, 96–98% of iron. It was shown, that it is reasonable to use the manganese concentrate for low phosphor metal manganese smelting, that will enable to decrease the dependence from manganese-containing materials import. A technology of steel alloying by obtained nickel concentrate elaborated. The substitution of metal nickel by nickel concentrate will considerably reduce expenses for alloying. A technology of metalized iron production by a solid-phase reducing method from an iron concentrate also elaborated, which will enable to decrease impurities content in steel during its application.
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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 | 0.001 | 0.001 |
| 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".