Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it