Water reuse to free up freshwater for higher‐value use and increase climate resilience and water productivity
Bibliographic record
Abstract
Abstract The impact of climate change on the availability of water affects all types of land use and sectors. This complexity calls for integrated water resources management and negotiations between sectors on the most important, cost‐effective, and productive allocation of water where it is a limited resource. This reflection paper shows examples of adaptation efforts to water scarcity at a scale where gains in water productivity can be derived from inter‐sectoral water reuse and wastewater–freshwater swaps, complementing other water scarcity coping strategies (water savings, long‐distance transfer, and desalination). Wastewater treatment for reuse offers opportunities across scales as it allows, for example, donor regions to be compensated with reclaimed water for the release of freshwater for higher‐value use, increasing overall economic water productivity in this way. In such water swaps, farmers are compensated with higher water volumes in exchange for higher quality. The reuse of water between sectors offers opportunities to (i) expand the traditional (agricultural) water productivity concept and (ii) significantly increase water productivity at the system level. While rural–urban water reallocation can help mitigate the impacts of climate change, compensating farmers with reclaimed water remains limited for the reasons discussed in the paper.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".