Water strategies and water–food Nexus: challenges and opportunities towards sustainable development in various regions of the World
Bibliographic record
Abstract
The twenty-first century is witnessing an explosion in global population, environmental changes, agricultural land disintegration, hunger, and geopolitical instabilities. It is difficult to manage these conditions or standardize improvement systems without thinking of the three main elements or subsystems that are necessary for any meaningful development-namely water (W), energy (E), and food (F). These key elements form what is globally agreed upon as the "WEF Nexus." While considering them, one should think about the other key factors that influence WEF Nexus, including population's growth, impacts of environmental changes (including climate change), moderation and adaptation regimes to climate change and climate resilience, loss of biodiversity, and sustainable nature. Together, the WEF Nexus subsystems represent a framework to ensure environmental protection that should be seen as an ethical and socioeconomic obligation. Issues, such as protection of water resources, and strategies and management tools or mechanisms for the use of water assets and agricultural innovations under the obligations of sustainable use, are investigated in this paper. Attention is paid to the relationship between water and food (WF Nexus) or water for food security in various world regions, including the Gulf Cooperation Council (GCC) countries, Central Asia countries and the Caucasus, China, Africa, and Canada. This paper also presents analyses of a great number of up-to-date publications regarding the "Nexus" perspective and its applications and limitations. This paper suggests that the Nexus' approach, in its different concepts (WEF, WE, WF and EF), can promote sustainable development and improve the quality of life of communities, while preserving natural, human, and social capital, addressing sustainability challenges, and protecting natural resources and the environment for long-term use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".