Market for Critical Raw Materials and its Influence on Mineral Prices
Bibliographic record
Abstract
The paper has focused on market for critical raw materials and its influence on mineral prices. Usually ores and ore products are deemed critical raw materials if they mostly or totally come from foreign countries, have difficult replacement, and are vital for the Nation’s economy, especially for defence issues. Tungsten, niobium, graphite and lithium were chosen for analysis from the critical mineral commodities declared by the European Commission and the Government of the Czech Republic. An analysis of these mineral commodity market conditions has been made, and their impacts on particular mineral availability and price have been assessed. As regards tungsten supplies, there are relatively many producer countries with the existing or developing extraction structures, but China has at its disposal 60% of the deposits. Lithium reserves are sufficient, but supplies are highly concentrated – four producer companies deliver about 90% of lithium in the world. Also niobium supplies are extremely concentrated, in the period, 2009–2012, two Brazilian mines and a single Canadian one produced 99% of niobium in the world. The biggest world producer of natural graphite is China that dominates 70% of the market. Natural resources of the above mentioned mineral commodities are not critical. The Earth’s crust deposits are sufficient for long - -term exploitation, and what’s more, a technology has been patented for lithium recycling. What rather matters is the issue of the free play of market forces. The theoretical preconditions for the free play of market forces and balanced price convergence – market presence of many various producers and many customers – are disturbed by producer structure, high concentration of mining com - panies and countries. Free market interference is implied in dominance of individual producer countries or production companies, and their ability to decide about production levels and related prices. Nevertheless, the inevitable rise of mineral commodity prices will mean that exploitation of some sources, which are currently deemed uneconomical, may become interesting.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".