IDENTIFYING THE PRIORITY APPROACH OF SALES ACTIVITIES FOR IRON-ORE ENTERPRISES
Bibliographic record
Abstract
The market of iron-ore raw materials characterized as a market oligopoly competition dominated by three multinational Corporation (Vale, Rio Tinto and BHP Billiton).Their share in world trade is about 61%, that allows to dictate pricing in the market.The company LKAB (Sweden), SNIM (Mauritania), CVG (Venezuela), Kumba (South Africa), Quebec Cartier (Canada) Metaloinvest (Russia), Metinvest (Ukraine) and Ferrexpo (OJSC "Poltava ore "(PGOK), Ukraine) has also a significant share in world trade.In recent years these companies have increased exports and diversified geographical structure.Poltava Mining (PGOK) -is export-oriented enterprise that sells almost all products in the foreign market (East and Central Europe, Asia), that competes with Northern Mining, Russian mills of Metalloinvest and Karelia pellets and suppliers from Sweden, Brazil, Australia, South Africa, India and Canada.The main advantages of PGOK in comparison with competitors it should be attributed favorable geographical location of the plant and infrastructure for shipments of pellets, large reserves of ore and high-tech facilities for its processing; favorable strategic partnerships with key customers, the industry average level of production costs and delivery of pellets.The export of Poltava GOK in 2011 increased by 5.4% to 9.506 million tons, of which 49% -is flotation pellets with Fe content 65%.It should be noted that the production of flotation pellets this year increased by 4.8% to 4.256 million tons, however the production of pellets with Fe content 62% -fell by 3.3% to 4.807 million tons This indicates a high consumer demand for more high-quality raw materials that is prepared and PGOK focusing to meet the needs of their clients.However, the decline of global economic conditions and market of steel and iron ore pellets made the majority of European consumers to reduce their production capacity and, accordingly, purchasing of raw materials.Therefore, the volume of pellets deliveries of PGOK market in Eastern and Central Europe in 2011 fell by 20% in comparison with 2010 to 5.263 million tons, and its share has fallen from 73% to 55% of the total exports of the plant (Table 1).It should be noted that last year Poltava GOK in general worked at full capacity, despite the adverse market conditions and the external pressure from the internal economic constraints (the growth of annual production inflation since 2011 on 15.6%, the tariff for electricity and gas etc.).However, PGOK compensated growth of average cost of output by increasing the production volume and efficiency,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".