PROSPECTIVE ASSESSMENT OF RESOURCES OF ALTERNATIVE TYPES OF ALUMINOSILICATE REFRACTORY RAW MATERIALS OF THE FAR EASTERN REGION OF RUSSIA
Bibliographic record
Abstract
The presence of a refractory industry characterizes the degree of industrialization. countries. Of the more than two hundred countries in the world, there is a developed refractory industry in about 35 countries. In Russia, the extraction and production of natural refractory raw materials is carried out in small quantities, amounting to units of percent of the total required quantity. The main raw material for its production is scarce and expensive alumina imported from China and other countries, 2/3 of which fall on aluminosili-cate refractories. According to the results of regional and thematic works on the territory of the Far Eastern Region of Russia, the scales of development of high-alumina formations of various genetic types have been determined: sedimentary-metamorphic (sillimanite, distene, andalusian), hydrothermal-metasomatic (alunite, alunite-dickite, alunite-diasporic), sedimentary (kaolinite), magmatic (anorthosite, labrador-anorthosite, nepheline and leucite), according to physico-chemical and technological properties (alumina content of 20-40% or more, fire resistance over 1580°C), the results of industrial research satisfying the requirements for refractory raw materials of alternative types. The presented rather extensive factual material on natural types of fire-resistant raw materials in the Far East testifies to the great potential for the formation of a raw material base for the production of refractories, the prospects of which are quite definite for the economy.
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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 | 0.001 | 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) | 0.000 | 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".