Visualizing water‐energy nexus landscapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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 it