Trends in Demand of Urban Surface Water Extractions and in Situ Use Functions
Bibliographic record
Abstract
Abstract Scientific literature currently lacks comprehensive understanding of urban surface water use functions. This hampers sound analysis of the demand and potential supply of these functions. This study provides a comprehensive overview of potential use functions, by integrating knowledge from ecosystem services and integrated urban water management fields. Analysis of water-related management plans for Toronto and Amsterdam shows that surface water is currently being used for a variety of functions related to nutrition, energy, water regulation, recreation, symbolic use, transportation and floating buildings. Notably, many use functions involve in situ use, rather than water extractions. Interviewed water managers and spatial planners in both cities expect demand of most use functions to increase by 2040, especially demand for thermal energy extraction, recreation and transportation. Some identified novel demands, such as climate regulation and reuse of waste products from waterway maintenance. Increasing demand is mainly driven by urban growth, climate change and sustainability ambitions. This study found urban surface water uses that are usually not acknowledged in scientific literature on urban water management. This comprehensive overview supports planning, design, and maintenance of urban surface waters, laying the foundation for future research on supply and demand of urban water use functions.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".