Global antimony supply risk assessment through the industry chain
Why this work is in the frame
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Bibliographic record
Abstract
Antimony is a type of critical metal for the energy transition. The antimony industry chain is distributed among the major developed and developing countries around the world. With the development of clean energy technology, the demand for antimony in photovoltaic and energy storage fields will increase significantly. Considering the significant changes in the global demand for antimony products and the serious supply shortage, people should pay more attention to the supply risk of related products of the antimony industry chain. In this paper, we propose a new integrated index to evaluate the supply risk of antimony industry chain related products, including Herfindahl Hirschman index, global governance index, human development index, global innovation index, and betweenness centrality in complex networks. Meanwhile, seven commodities in the antimony industry chain are selected for empirical analysis from 2011 to 2019. The results show that countries with high supply risks of the industry chain upstream include Canada, France, Germany, India, Japan, Thailand, and the United Kingdom. And, Australia, India, Japan, Thailand, and Vietnam are with high supply risks in the midstream of the industry chain. Meanwhile, Canada, India, Japan, and Thailand are with high downstream supply risks. Some countries, like China, the United States, and Germany, play a core role in different sectors of the industry chain. International competitive relations of countries have caused a high supply risk of products related to the antimony industry chain. The supply risk of the antimony industry chain shows that countries must strengthen industrial division and cooperation to maximize their interests. It is suggested to take the country-specific measures to mitigate supply risks, including establishing national inventories of critical materials, overseas investment, strengthening the guidance of industrial policies, and accelerating infrastructure construction.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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