Investigating the Characteristics of Uranium Trade Flows and Trade Evolution along the Supply Chain
Bibliographic record
Abstract
Nuclear energy is essential for national energy security and clean energy transition, which has been defined as a critical mineral by many countries.However, the supply of nuclear fuel in the international market is complex.Currently, the trade flow and trade pattern characteristics of the uranium supply chain have not been clarified, and insufficient attention has been paid.To explore this issue, this paper constructs a multilayer complex network model of uranium supply chain based on uranium product trade data from 2014 to 2023 through complex network methods.Then, we focus on the topological characteristics, trade flow, trade pattern evolution characteristics of the multi-layer trade supply chain network of uranium (MTCN), and the importance of major trading countries in the network.The results show: (1) international trade in the global uranium supply chain is of a "boom-to-fade" characterization, with the number of participating countries, trade relations and trade volumes decreasing, while the main hub countries are fixed.(2) The global uranium trade flow shows a significant centralized characterization, with the characteristic of large to small along the supply chain.Besides, the main trade is concentrated in the upstream natural uranium, and it has formed a "Kazakhstan-Canada" supply center and a "China-USA-France" demand center.(3) Importing countries, such as Russia and USA, show strong resource control in uranium supply chain trade.It expects that the findings of this paper will help countries involved in uranium trading to develop sustainable resource management policies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".