The Canadian National Water and Wastewater Benchmarking Initiative. Using process to drive improvement: strategic management of water in urban areas
Bibliographic record
Abstract
Earth Tech has been successfully benchmarking Canadian municipal water, wastewater and stormwater utility operations since 1997. While the fundamental purpose of this project was metric benchmarking for the purpose of making performance comparisons to guide continuous improvement, the project is now serving as a dynamic platform to consider, examine, and implement a broad range of utility best practices that have resulted in superior performance where they have been implemented. The keys to success, however, were based more on a process that emphasizes communication, teamwork, and collaboration rather than the trend to push computerized data management systems to their fullest potential, and most importantly, in recognizing the importance of ‘hard work’. With these success factors now well understood and documented, it is feasible to benchmark almost any public infrastructure amongst agencies that are willing, regardless of their level of technological development. Finally, by sharing this methodology, the performance measure descriptions and detailed definitions, it is also feasible to make international comparisons in a simple and cost effective manner, thus opening the door to the broad exchange of international best practices.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".