Climate-Land-Energy-Water Nexus Models Across Scales: Progress, Gaps and Best Accessibility Practices
Bibliographic record
Abstract
Approaches that integrate feedback between climate, land, energy and water (CLEW) have progressed significantly in scope and complexity. The so-called nexus approaches have shown their usefulness in assessing strategies to achieve the Sustainable Development Goals in the contexts of increasing demands, resource scarcity, and climate change. However, most nexus analyses omit some important inter-linkages that could actually be addressed. The omissions often stem from technical and practical considerations, but also from limited dissemination of new open-source frameworks incorporating recent advances. We review and present a set of models that can meet the needs of decision makers for analysis tools capable of addressing a broad range of nexus questions. Particular attention is given to model accessibility, usability and community support. The other objective of this review is to discuss research gaps, and critical needs and opportunities for further model development from a scientific viewpoint. We explore at different scales where and why some nexus interactions are most relevant. We find that both very small scale and global models tend to neglect some CLEW interactions, but for different reasons. The former rarely include climate impacts, which are often marginal at the local level, while the latter mostly lack some aspects because of the complexity of large full CLEW systems at the global level.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".