Enhancing water efficiency programming in the City of Calgary
Bibliographic record
Abstract
ABSTRACT This paper presents the steps undertaken in a two-phase study to enhance the City of Calgary's (the City) water efficiency programming for indoor and outdoor industrial, commercial, and institutional (ICI) customers and outdoor residential customers. Study objectives included evaluating programs in other urban jurisdictions for suitability to Calgary, and developing short-, medium-, and long-term recommendations for water efficiency programming for the City. Phase I of the study included a literature review of Calgary's water consumption trends and explored over 150 water efficiency programs implemented across jurisdictions in North America, identifying a subset of 33 programs for further evaluation. Phase II evaluated program options through an integrated assessment, including gap, Strengths, Weaknesses, Opportunities, and Threats, and cost-benefit analysis. An implementation strategy was developed for seven water efficiency programs, grouped into complementary bundles of indoor and outdoor ICI and residential landscape transformation programs. The study also identified areas for further research, and key supporting elements or success factors for water efficiency programming in the City. This paper adds value to the discussion on approaches to select suitable indoor and outdoor ICI and outdoor residential water efficiency programs, and aids in informing the City's current and future strategic water planning and programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".