Transfer of Virtual Water of Woody Forest Products from China
Bibliographic record
Abstract
Global freshwater resources are under increasing pressure. It is reported that international trade of water-intensive products (the so-called virtual water trade) can be used to ease global water pressure. In spite of the significant amount of international trade of woody forest products, virtual water of woody forest products (VWWFP) and the corresponding international trade are largely ignored. However, virtual water research has progressed steadily. This study maps VWWFP and statistically analyzes China’s official data for the period 1993–2014. The results show a rapid increase in the trend of VWWFP flow from China, reaching 7.61 × 1012 m3 or 3.48 times annual virtual water trade for agricultural products. The export and import volumes of China are respectively 1.27 × 1012 m3 and 6.34 × 1012 m3 for 1993–2014. China imported a total of 5.07 × 1012 m3 of VWWFP in 1993–2014 to lessen domestic water pressure, which is five times the annual water transfer via China’s South–North Water Transfer project. Asia and Europe account for the highest contribution (50.52%) to China’s import. Other contributors include the Russian Federation (16.63%), Indonesia (13.45%), Canada (13.41%), the United States of America (9.60%), Brazil (7.23%) and Malaysia (6.33%). China mainly exports VWWFP to Asia (47.68%), North America (23.24%), and Europe (20.01%). The countries which export the highest amount of VWWFP include the United States of America, Japan, Republic of Korea and Canada. Then the countries which import the highest amount of VWWFP include the Russian Federation, Canada, United States of America, and Brazil. The VWWFP flow study shows an obvious geographical distribution that is driven by proximity and traffic since transportation cost of woody forest products could be significant.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".