Analysis of the characteristics of global virtual water trade network using degree and eigenvector centrality, with a focus on food and feed crops
Bibliographic record
Abstract
Abstract. This study aims to analyse the characteristics of global virtual water trade (GVWT) such as connectivity of each trader, vulnerable importers, and influential countries using degree and eigenvector centrality during the period 2006–2010. The degree centrality was used to measure the connectivity and eigenvector centrality was used to measure the influence on entire GVWT network. Mexico, Egypt, China, Korea Rep., and Japan were classified to vulnerable importers because they imported a lot of virtual water with the low connectivity. Especially, Egypt had 15.3 Gm³ year-1 blue water savings effects through GVWT, thus the vulnerable structure could cause the water shortage problem in importer. The entire GVWT network could be changed by a few nodes which call influential traders, and we figured out the influential traders using eigenvector centrality. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high eigenvector with a large volume of green water trade. In case of blue water trade, western Asia, Pakistan, and India had high eigenvector centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used the high proportion of internal water resource for virtual water export (32.9 and 25.1 %), thus rest of traders should consider the water resource management in these exporters carefully.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".