Water-energy-food nexus as a new approach for watershed resources management: a review
Bibliographic record
Abstract
The Water-Energy-Food nexus (WEF) has been initially introduced in the international community as an adaptive management approach in response to climate change. This study aims to review and analyze the existing literature on WEF nexus approach at different scales and to suggest supplementary ideas for better applicability of WEF nexus framework in integrated watershed management. In terms of geographical distribution, the study covers Asia (Central, South, Southeast, and East), Australia, Africa (North, South, and East), North America (USA, Mexico, and Canada), South America (Brazil), Europe (UK, Italy, Germany, Spain, Sweden, and Greece), and Oceania.For this, 203 articles and documents were found dealing with WEF nexus. Interest over time in WEF nexus was examined from 2011 to 2019 in these regions. The review showed 10 articles had a close linkage with water–food, 49 with water-energy, 119 with water-energy-food, six with water-food-energy-ecosystems, five with water-energy-land-food, three with food-energy-environment, three with water-soil-waste and eight with climate. We propose ecosystem services and other important commodities like soil be considered in future nexus relevant studies. Towards this, the soil-water-energy-food (SWEF) nexus is introduced as a useful approach towards higher sustainability and adaptive management at the watershed scale.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".