FORMATION OF PRODUCTION GROWTH POINTS ON THE BASIS OF MINERAL - RAW MATERIAL RESOURCES AS A FACTOR OF IMPROVEMENT OF THE TERRITORIAL STRUCTURE OF THE INDUSTRY OF THE REPUBLIC OF KARAKALPAKSTAN
Bibliographic record
Abstract
The use of mineral resources plays an important role in the global economy. “As noted in the British newspaper“ Financial Times ”, this sector ranks 1st in the world in terms of capitalization of the largest companies, including mining itself (excluding oil and gas) - 5th place among global industries after the banking sector, oil and gas industry, pharmaceutical and computer industries"(Kondratyev, 2014). In the developed and rapidly developing countries of the world, industrial growth is achieved through the effective use of the local potential of natural resources, improvement of the structural composition of the industry. According to the World Bank, in 2018 the share of mineral resources in GDP was 0,9 percent in Canada, 3,5 percent in Australia and 2,5 percent in Brazil, while in Uzbekistan the figure was 12,3 percent (Saydaxmedov, 2020). Many large scientific centers around the world are working on changing the methodology for the economic assessment of mineral resources, taking into account the regional economy, new economic geography, changes in the subjects of the institutional economy and the growth of knowledge that has occurred in recent years. Much attention is paid to the use of socio-economic indicators along with technical and economic indicators in assessing the mineral resource base. Consequently, due to the development of mineral resources, opportunities arise for creating new jobs, increasing the income of the population, introducing innovative ideas and technologies in practice, and creating a competitive environment in the economy. Therefore, the study of problems in this area in connection with the social sphere and institutions acquires the necessary scientific significance. The article discusses the formation of points of production growth. The main directions of the formation of points of production growth based on mineral-raw material resources are being studied. The distribution of mineral-raw material resources by zones of Karakalpakstan is investigated. In addition, the article talks about the specific features of the formation of reference points of growth. The stages of the formation of growth support points based on the local mineral-raw material resources of Karakalpakstan in 2020-2030 are also considered.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".