Development of Decision Support Tool for Evaluation of Urban Water System Metabolism Efficiency
Bibliographic record
Abstract
The revitalization of Toronto’s waterfront presents the largest urban redevelopment project currently underway in North America. With respect to planning the waterfront’s urban water systems (UWS), a number of studies considered a range of criteria in search for sustainable alternatives. However, a comprehensive assessment of the integrated source-drinking-wastewater-stormwater systems over their life cycles has not been developed. According to the main postulates of the integrated approach, hybrid water systems can offer potentially more sustainable solutions than traditional centralized systems. This paper discusses the development process of a decision support tool designed to facilitate evaluation of alternatives based on UWS metabolism concept while addressing some typical challenges of hydroinformatics. This decision-making support tool analyses and compares the sustainability performance of alternative decentralized solutions against a baseline conventional approach on a neighbourhood level. The tool uses a set of criteria, adopted by the large group of stakeholders involved in the development process, that are not typically considered in the decision-making process, such as energy savings, greenhouse gas (GHG) emissions, climate change resiliency, chemical use, and nutrient recovery.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".