Ecological Network Analysis for Water Embodied in Global Agricultural Products Trade
Bibliographic record
Abstract
Global agricultural products trade, which is inextricably related with freshwater consumption, has been an important part of international economic network correlated with resource redistribution. This study estimates embodied agricultural freshwater utilization of each countries along the global supply chain based on a multi-regional input-output (MRIO) model. Monetary MRIO for year 2011, containing 188-economy, 26-sector, is conducted to expound global embodied agricultural freshwater use flows as irrigation water requirements of 160 crops in different regions are investigated. Furthermore, ecological network analysis (ENA) is performed to investigate the mutual interactions within economies as well as the dominant economies and pathways for embodied freshwater consumption via agricultural product trade. Results show that, in 20112, several major large developed economies are the decisive drivers of embodied agricultural freshwater utilization globally. The evaluation of the water embodied global agricultural products trade investigates that the embodied water mainly transfers from large developed economies, including USA, Canada, and Russian Federation, to developing regions like India and African Union. And another direction for embodied water flow is from the biggest developing regions China to regions like Korea, European Union, India and Mexico. This paper may provide a new perspective on formulating the global water management strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".