A clustering approach to improve spatial representation in water-energy-food models
Bibliographic record
Abstract
Abstract Currently available water-energy-food (WEF) modelling frameworks to analyse cross-sectoral interactions often share one or more of the following gaps: (a) lack of integration between sectors, (b) coarse spatial representation, and (c) lack of reproducible methods of nexus assessment. In this paper, we present a novel clustering tool as an expansion to the Climate-Land-Energy-Water-Systems modelling framework used to quantify inter-sectoral linkages between water, energy, and food systems. The clustering tool uses Agglomerative Hierarchical clustering to aggregate spatial data related to the land and water sectors. Using clusters of aggregated data reconciles the need for a spatially resolved representation of the land-use and water sectors with the computational and data requirements to efficiently solve such a model. The aggregated clusters, combined together with energy system components, form an integrated resource planning structure. The modelling framework is underpinned by an open-source energy system modelling tool—OSeMOSYS—and uses publicly available data with global coverage. By doing so, the modelling framework allows for reproducible WEF nexus assessments. The approach is used to explore the inter-sectoral linkages between the energy, land-use, and water sectors of Viet Nam out to 2030. A validation of the clustering approach confirms that underlying trends actual crop yield data are preserved in the resultant clusters. Finally, changes in cultivated area of selected crops are observed and differences in levels of crop migration are identified.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".