Supporting the implementation of effective urban water conservation and demand management strategies
Bibliographic record
Abstract
There is an urgent need to ensure the sustainability of urban water resources. In the face of growing challenges including urbanization, climate change, and increased competition for available resources, new and innovative water management strategies are required. The conventional approach to sustainable urban water management typically focusses on the supply dimension; however, this has proven to be largely inadequate and many are calling for a new approach to addressing this issue. The aim of this thesis is to examine how the water meter data management and analysis systems might be improved to better support water conservation efforts by exploring the literature and carrying out a case study of the City of Vancouver. Literature covering the field of urban water demand modeling as well as conservation interventions and their use in reducing potable water demand were reviewed within the context of the changing understanding of urban water resource sustainability and its dimensions. A case study of the City of Vancouver parks system then explored how existing water meter data could be leveraged to support conservation efforts. The results found that while there have been substantial efforts undertaken to characterize and understand the factors that influence water demand, behaviour and social factors remain largely unaccounted for which are vital dimensions to include in the development of solutions. The case study findings demonstrated that the analysis of existing data can be successful in understanding conservation strategy options, which can be a useful entry point in addressing this highly complex problem. Across the literature and case study the findings highlight the gap in knowledge around water use and behaviour that is evident when the focus is on sustainability. Future work is recommended to incorporate a wide range of influencing factors that go beyond the conventional supply oriented paradigm.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".