Urban Water Security Dashboard: Systems Approach to Characterizing the Water Security of Cities
Bibliographic record
Abstract
Urban water security is a major concern in the context of urbanization and climate change. Water security goes beyond having good infrastructure or good governance. Systems thinking can help in understanding the mechanisms that influence the long-term water security of a city. Therefore, we developed a dashboard of 56 indicators based on the pressure-state-impact-response (PSIR) framework. We applied the dashboard to ten cities to capture different characteristics of their water security and ranked the cities based on their overall water security index score. We found the highest levels of water security in wealthy cities in water-abundant environments (Amsterdam and Toronto), in which security is determined by the ability of the city to mitigate flood risks and the sustainability of hinterland dependencies for water supply. The lowest security was found in developing cities (Nairobi, Lima, and Jakarta). Here, the combination of large socioeconomic pressures (e.g., rapid population growth, slums, low GDP, polluting industries) and an inadequate response (weak institutions, and poor planning and operational management) leads to inappropriate fulfilment of all functions of the urban water system.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".