Catalyzing sustainable development goals through the water-energy-food nexus
Bibliographic record
Abstract
Water, energy, and food (WEF) are central to sustainable development as they are vital for socio-ecological and socio-economic sustainability and human and environmental wellbeing. How the three are used and managed is central to either the aggravation of climate change or the enhancement of resilience and adaptation strategies. This mixed transdisciplinary study developed a WEF nexus-based framework to guide strategic policy decisions to catalyze progress toward achieving sustainable development goals. The aim is to guide the cross-sectoral management of resources for sustainable development under climate change, increasing demand, depletion, degradation, and uncertainty. Past and present data on resource management was assessed to comprehend future availability toward achieving sustainable development outcomes for people and the planet. The fundamentals of holistic WEF resources management were assessed, highlighting the significance of transformative, cross-sectoral, and circular approaches in enhancing resource use efficiency and sustainability. This is critical for informing sustainable natural resources management decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".