Assessment of competitive potential of iron ore mines — a basis of formation of strategy of their development in the conditions of globalization
Bibliographic record
Abstract
The course of economical development of Ukraine predetermines its integration in the global economy. Iron ore mines of Ukraine are the main raw material supply for the iron industry in the country and for large exporters of iron ore products to many countries of the world. Nevertheless, at the cost and quality of the iron ore products, Ukraine gives way to the world’s leaders in this area (Sweden, Canada, Australia, etc.). The authors have made an attempt to understand the component-by-component structure of competitive potential and to reveal its growth opportunities in Ukraine as against competitive potential of the world’s top mines (which is conventionally assumed as 100%). The work tool of the analysis is the method of expert appraisement of influence exerted by each component on factual competitive ability of a mine. From the comparison of the estimated figures of the influence characteristics obtained for the same type mines in the world’s leading countries and in Ukraine, it is found that the main cause of retardation of Ukrainian mines and mining-and-processing integrated works lies in the management–engineering sphere of their performance. On the whole, one-type mines in Ukraine use their competitive potential merely by 63.2%. The upbuilding of the competitive potential is the prime way of achieving improvement in the iron ore industry performance in Ukraine. This article is published in the order of discussion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".