Research on the supply risk propagation in the global iron ore trade network
Bibliographic record
Abstract
Iron ore serves as a critical resource underpinning global industrialization, extensively utilized in steel production and infrastructure development. Amid increasing complexities in the global economic landscape, risks and uncertainties within iron ore supply chains have intensified, particularly under the influence of geopolitical conflicts and trade protectionism. Leveraging 2023 iron ore trade data, this study constructs a global iron ore trade network using complex network theory and develops a cascading failure model to assess systemic vulnerabilities. Key findings include: ⅰ:The iron ore trade system exhibits a centralized structure dominated by China, Australia, and Brazil, resulting in elevated supply risks. Supply disruptions could propagate crises, potentially disrupting supply chains in over 40% of participating nations.ⅱ:Community 1 (China, Australia, Brazil) accounts for 90% of trade volume and demonstrates heightened susceptibility to cascading failures. In contrast, Community 2 (Canada, Germany, South Africa) mitigates crisis propagation through diversified supply strategies. Enhanced cross-community linkages facilitated by nations like India reduce systemic risks. ⅲ:Critical node failures yield disproportionate impacts: Increasing the risk resilience parameter β from 0.2 to 0.4 reduces cascade magnitude by 62%. While Brazilian disruptions trigger extensive spatial propagation, Australia’s export concentration renders downstream industries more vulnerable to paralysis despite narrower geographic impacts. Based on the evaluation results of the global iron ore trade network, relevant suggestions such as developing emerging supply sources and constructing a deduction system were put forward.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".