Magnesita keenly waits on graphite, talc, projects
Bibliographic record
Abstract
[Octavio Cortes Pereira Lopes] also admitted that the company, which sells refractory products to steel and cement manufacturers globally, was facing a worse backdrop in the Brazilian refractories market than had originally been forecast, but said that, on the flip side, demand from the European steel industry was exceeding expectations. In the first quarter of 2014, the macroeconomic environment continued to perform poorly. In Brazil, confidence indicators have worsened with low growth, inflationary pressures, rising interest rates and the risk of energy rationing. According to the Central Bank's Focus survey, Brazil's GDP is expected to grow no more than 1.65% this year, he said. Overall, the volume of refractories Magnesita sold in Q1 2014 reached 214,000 tonnes, 5.3% higher than Q1 2013, despite a 0.4% decrease in steel production in South America and meagre 0.8% growth in North America, the company outlined.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".