Chrysotile-asbestos industry – production from mining to enrichment
Bibliographic record
Abstract
В статье дается анализ состояния и перспектив развития хризотил-асбестовой отрасли в России, Казахстане, Канаде, Бразилии и других странах, диверсификации этих производств, применения комплексной переработки при добыче и обогащении хризотил-асбестовых руд. Описаны процессы серпентинизации и асбестообразования на месторождениях. Даны характеристики геолого-промышленных типов месторождений хризотил-асбеста, показаны достоинства месторождений хризотил-асбеста Баженовского подтипа. Рассматриваются методы и технологии добычи, обогащения, переработки хризотил-асбестовых руд, а также применение хризотил-асбеста в промышленности. Приведен обзор рынка производства и потребления хризотил-асбеста и фракционного щебня. The article provides an analysis of the state and prospects for the development of the chrysotile-asbestos industry in Russia, Kazakhstan, Canada, Brazil and other countries, the diversification of these industries, the use of complex processing in the extraction and enrichment of chrysotile-asbestos ores. The processes of serpentinization and asbestos formation in the deposits are described. The characteristics of geological and industrial types of chrysotile-asbestos deposits are given, the advantages of chrysotile-asbestos deposits of the Bazhenov subtype are shown. The methods and technologies of extraction, enrichment, processing of chrysotile-asbestos ores, as well as the use of chrysotile-asbestos in industry are considered. An overview of the market for the production and consumption of chrysotile asbestos and fractional crushed stone is given.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".