Bibliographic record
Abstract
The analysis of the rolled metal market shows that major market players can predict further pricing changes stipulated by challenging political and economic situation in the world. This article focuses on the main factors that influenced the cost of metal at the end of 2014, 2015 and early 2016 and contributed to further price fluctuation. In the new economic environment the world metal market faces dramatic changes. There arise new pricing reforms aiming diversion from a speculative component to a real market price. On the results of 2014, deflation of prices on metal made, by different sources, 12-15% compared to prices at the beginning of the year. Thus, the outlining tendencies force major Russian steel traders (e. g. EVRAZ, MMC, MIC etc.) to redirect their sales from the territory of the Russian Federation to abroad (Europe, Asia, America). According to steel output, in the first quarter of 2015 Russia remained the fifth country in the world. In the nearest future forecasts about steel production in the leading countries-producers don’t estimate any significant growth. The only exception, according to the experts, is a steel market in India, which is actively developing. Domestic product consumption in this sector defines growth rates of metallurgic industry in the mid-term perspective, according to the facts presented by the Ministry of Economic Development.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".