Climate Land Energy Water nexus models reviewed across scales: progress, gaps and best accessibility practices
Bibliographic record
Abstract
Approaches that integrate feedbacks between climate, land, energy and water (CLEW) have increasingly advanced and have become more complex. Such so called nexus approaches have already been useful in quantitatively assessing strategies under resource scarcity, planning infrastructure for achieving the Sustainable Development Goals or assessing cross-sectoral climate change impacts. However, most of the models and frameworks do often miss some important inter-linkages that could actually be addressed by using newest models. The reason for such negligence is often technical and practical, as many of the newly developed and open-source frameworks are not yet widespread. We review and present these models so that decision maker needing tools for analysis could identify what is best for their needs. Particular attention is given to model usability, accessibility, longevity and community support. At the same time we discuss research gaps, and room for improvement for next development of the models from a scientific point of view. We explore at different scales where and why some nexus interaction are most relevant. We find that both very small scale and global model tend to neglect some CLEW interaction, but for different reasons. The first rarely include climate impacts, which are often marginal at local level. While the latter mostly lack pieces because of the complexity of large full CLEW system at the global level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".