Editorial: Water-Energy-Food-Health Solutions and innovations for low-carbon, climate-resilient drylands
Bibliographic record
Abstract
Editorial on the Research Topic Water-Energy-Food-Health Solutions and innovations for low-carbon, climate-resilient drylandsWe live in a world of complex and tightly interconnected grand challenges that threaten the sustainability of our societies.Examples of such challenges are summed up in the United Nations Sustainable Development Goals (UN-SDGs), which include specific goals to address water, energy, and food insecurities.Our ability to address these challenges depends on our readiness to collaborate across disciplines and sectors to reimagine thriving, healthy, resilient societies that respect the boundaries and health of our planet.As nations work toward implementing the UN-SDGs, we need to support decision makers, create synergies, and avoid unintended competition between societal goals.Thus, the urgent need for innovative simulation and assessment tools and governance models to represent these complex systems in an accessible manner.Drylands face important resource gaps including access to water, food, energy, nutrition, and healthcare.These gaps are expected to increase with demographic conflicts and climate change.The highly interlinked primary resources carry high risks and vulnerabilities.Understanding these interlinkages and associated risks and vulnerabilities to better comprehend the complex system of systems they represent is crucial and requires multidisciplinary work that encompasses technologies, science, policies, health, communication, and socioeconomics at both local process and system-level scales.In 2018, the American University of Beirut (AUB) launched WEFRAH: the Water-Energy-Food-Health Nexus of Renewable Resources initiative.WEFRAH comprises one of the largest research communities in the Middle Eastern North Africa (MENA) region.It is a university-wide initiative led by the Faculty of Agricultural and Food Sciences.WEFRAH includes a critical mass of faculty from disciplines across the University whose focus is collaboration to achieve security of primary resources.Its core conviction is achieving water,
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.040 | 0.027 |
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".