Visualizing water‐energy nexus landscapes
Bibliographic record
Abstract
Abstract Over the past decade, the water‐energy nexus (WEN) has emerged as a prominent framework with which to analyze and visualize interconnections between energy production, freshwater resources, and the hydrological cycle. The WEN is a fundamentally geographic concept embedded in landscapes. WEN analyses often include landscape visualizations, yet these are rarely conceptually rigorous; consequently, the visual‐representational dimensions of WEN analyses remain relatively weak. Our paper addresses this gap through a meta‐review of 503 WEN visualizations sourced from 336 scholarly articles. Based on this analysis, we argue that WEN visualizations often depict complex landscapes as technical systems, while eliding broader considerations of the multiscalar, spatiotemporal, and hydrosocial dimensions of water and energy. In response to these limitations, we offer an alternative approach to visualizing hydrosocial landscapes that draws upon parallel work in geography and cognate disciplines. In the concluding section of the paper, we formulate a set of interdisciplinary recommendations to guide the production of more theoretically‐informed nexus visualizations grounded in the concepts of spatiality, temporality, and hydrosociality. The article is categorized under: Engineering Water > Planning Water Human Water > Methods Water and Life > Conservation, Management, and Awareness Engineering Water > Methods
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".