DEMAND SIDE MANAGEMENT OF WATER IN ONTARIO MUNICIPALITIES: STATUS, PROGRESS, AND OPPORTUNITIES<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT: Demand side management is being used increasingly by Ontario municipalities as a way to improve the efficiency of water use, defer the costs associated with constructing new water treatment works, and minimize the environmental impacts associated with supplying water. A comprehensive survey of 153 Ontario municipalities was completed in mid‐1998. These ranged in size from small rural townships (with populations as low as 500 people) to the province's largest urban center, Metropolitan Toronto, with a population of approximately 2.5 million people. The questionnaire measured the use of six broad types of demand side measures, including water pricing and metering; municipal by‐laws (ordinances) that promote water conservation; operational and maintenance measures to reduce water losses and consumption; water‐saving plumbing fixtures and devices; public participation programs that encourage water conservation; and other measures, such as water audits. Additionally, the survey collected data on implementation barriers and opportunities. Since the last comprehensive Ontario survey, conducted in 1987 by Kreutzwiser and Fea‐gan (1989), there has been an increase in the use of basic tools such as metering and pricing, plumbing fixtures, and public participation programs. Additionally, new initiatives, such as water audits and computerized monitoring equipment, are being used. However, in many areas opportunities exist to make better use of demand side measures. Unfortunately, municipal capacity to do so often is constrained by (among other factors) limited finances, lack of political will, and public resistance. Demonstration of real cost savings to consumers, and the development of specific goals and objectives for demand side management programs, are two important steps needed to overcome these challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".