Toward a formal definition of water scarcity in natural‐human systems
Bibliographic record
Abstract
Water scarcity may appear to be a simple concept, but it can be difficult to apply to complex natural‐human systems. While aggregate scarcity indices are straightforward to compute, they do not adequately represent the spatial and temporal variations in water scarcity that arise from complex systems interactions. The uncertain effects of future climate change on water scarcity add to the need for clarity on the concept of water scarcity. Starting with a simple but robust definition—the marginal value of a unit of water we—highlight key aspects of water scarcity and illustrate its many biophysical and socioeconomic determinants. We make four central observations. First, water scarcity varies greatly across location, time, and a multitude of uses that are valued either directly or indirectly by society. Second, water scarcity is fundamentally a normative, anthropocentric concept and, thus, can and should be distinguished from the related, purely descriptive notion of water deficit. While such an anthropocentric perspective may seem limiting, it has the potential to encompass the vast range of interests that society has in water. Third, our ability to understand and anticipate changes in water scarcity requires distinguishing between the factors that affect the value or benefits of water from those affecting the costs of transforming water in space, time and form. Finally, this robust and rigorous definition of water scarcity will facilitate better communication and understanding for both policymakers and scientists.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".