Sustainable Water Resources Management through Disaggregated Multi-Region Virtual Water Flow and Interaction Analysis
Bibliographic record
Abstract
Abstract Virtual water transfer is considered as an important pathway to alleviate water shortage in arid regions due to limited water resources and unbalanced distribution. It is essential to explore the hidden mechanism of virtual water transfers among multiple regions to support future water resources allocation and management. In this study, a Disaggregated Multi-Region Virtual Water Flow and Interaction (DrWIn) model is developed to facilitate the analysis of virtual water inflow, outflow, transfer balance, and the related interactions. In addition, a factorial analysis is integrated to quantify the impacts of industrial water consumption and their interactive effects. A special case study of China is conducted to illustrate the applicability and superiority of the DrWIn model. It is found that interaction effects of any two industries are negative, indicating that high freshwater consumption in two industries is not the best choice. The obtained results provide a solid scientific basis for identifying the key industries and regions across a multi-region study system and supporting water resources utilization management in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".