Supply Security and Technological Innovation of Global Cobalt Minerals
Bibliographic record
Abstract
The worldwide cobalt ore trade and supply chain crisis has attracted the attention of many countries and institutions, in such a manner that major countries have listed it among their key mineral series or list. The world has huge reserves of cobalt resources, however, with limited production and presence of geopolitical risks. This is mainly manifested in the existence of the highly concentrated production，refinery and consumption of cobalt resources. In addition to seabed cobalt deposits, cobalt exists in the following forms in the continents: layered sedimentary copper-cobalt deposits (about 58%), mainly located in the Democratic Republic of Congo and Zambia; laterite-type nickel-cobalt deposits (about 29%), mainly situated in Australia, New Caledonia and Cuba; magmatic nickel-copper-PGE-cobalt deposits (about 9%), mainly distributed in Australia, Canada, Russia, Finland and the United States. Over 60% of the world’s cobalt concentrate is produced in D.R.Congo. But above 60% of cobalt ore refining and processing are done in China, while more than 60% of refined cobalt are consumed by China, the European Union and the United States. In particular, China, the United Kingdom, and Japan are nearly 100% dependent on import for cobalt supply. In face of such severe situation and intense competition, technological innovation is key. New technologies have actually been gradually applied, for example, the combined process of beneficiation and metallurgy, as well as some new extraction methods. Major challenges lie in the improvement of cobalt ore recovery rate, secondary resource utilization and product recycling. Many departments, institutions and enterprises in China have made long-term and in-depth arrangements in this regard. However, China’s international influence and control over the supply chain remain relatively weak. A big gap still exists as compared against the developed countries, making cobalt supply security an extremely important issue for China. Competition stays intense between China and western nations in terms of raw material supply. To improve the security of cobalt supply, it is crucial to gain a full understanding of the occurrence characteristics of the several major resource types, impose immense efforts on technological innovation in mining, ore processing and metallurgy, and push industrial innovation with high standards and high quality. In the meantime, considerable attention should also be paid to promoting international industrial alliances and technological cooperation, with a vision to get well prepared for the restructuring of the international industrial chain and changes in the competitive landscape.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".