Bibliographic record
Abstract
China now accounts for between 56% and 85% of total production of refractory grades of alumina, bauxite, magnesia and natural graphite. The country produced 56% of refractory alumina (calcined, tabular, and white fused) in 2011. In addition, Chinese producers accounted for 85% of the world's refractory bauxite (calcined and brown fused); 69% of natural graphite output; and 61% of dead burned and fused magnesia (Figure 2). In its 12 th Five-Year plan for the period between 2011 and 2015, the Chinese government announced several policies with similar implications for magnesia. Existing magnesia operations must have the potential to be scaled up to 1.5m tpa production capacity, while large and medium-sized enterprises are encouraged to acquire and consolidate small operations. On the technology front, calcining operations with rotary kilns must have systems to utilise waste heat, and shaft kilns must be gas-fired. Crucially, the plan states that production capacity of'old technology' must be reduced to 1,000 kilns by 2015, comprising 500 CCM, 300 FM and 200 DBM kilns. This is thought to be a push to eliminate overcapacity, which currently stands at around 5m tpa for refractory magnesia. Bauxite has been the focus of several development projects, the most advanced of which is UC Rusal's Timan Bauxite subsidiary in Komi, Russia. The company began initial production of refractory bauxite in 2012, with an output of 90,000 tonnes. Rusal expects to reach full production of 250,000 tpa in 2013. However, it is understood that refractory bauxite from Timan will be mainly for domestic consumption. Material for export markets is being developed in Guyana by Canadian company, First Bauxite. The company owns the Bonasika sintered bauxite project, from which it plans to produce 100,000 tpa beginning in 2014.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".