A path towards "one water" community : performance assessment and benchmarking
Bibliographic record
Abstract
An urban water system (UWS) has three main service components: (i) drinking water; (ii) wastewater; and (iii) stormwater. Historically, each component in urban water development evolved with different objectives for “different” types of water. Even today, the trend continues, as urban water services are managed in silos. The silo-based approaches are less sustainable, resilient, and reliable, mainly because of significant pressures on freshwater supplies exerted by the increasing population, high living standards, rapid urbanization, and climate change uncertainties. To cope with these challenges, conventional thinking needs to change. An innovative paradigm, the “One Water” approach (OWA), which considers “urban water” as a single entity, is the need of the hour. Currently, Australia, USA, and Singapore are leading the implementation of the OWA whereas the EU nations have emphasized the need for integrating water resource management (reflecting the One Water concept) in an urban environment. Only a few Canadian municipalities have embraced OWA at a very preliminary level. This research aimed to develop an OWA-based framework to improve urban water sustainability, resiliency, and reliability. The research entailed five phases. Phase 1 identified Key Performance Indicators (KPIs) to evaluate drinking water, wastewater, and stormwater performance individually. Phase 2 developed a performance assessment model that benchmarked UWS performance, determined weaknesses, and recommended necessary interventions to enhance overall performance. Phase 3 provided a bigger picture of OWA (definition and scope) concept, existing practices, and challenges to implementing OWA in a real-world environment. In addition, it also developed an optimization model to identify the optimum water conservation strategies to reduce potable water use. Phase 4 developed and prioritized OWA indicators to evaluate integrated UWS (IUWS) performance and measure water systems’ sustainability, resiliency, and reliability. Phase 5 adopted a design thinking approach to develop best practices in implementing OWA in UWS. The results of this research will provide foundational knowledge to evaluate individual water system performance and set performance benchmarks for small and medium-sized UWSs. In addition, the findings from this study will open new avenues for urban water managers to adopt OWA in their existing systems to evaluate their IUWS performance. Thus, it will assist policy-makers in establishing new guidelines to improve their UWS holistically and help decision-makers make strategic decisions to address an increasing urban water service demand.
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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 | 0.151 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".