Wastewater Asset Management at the City of Edmonton, Alberta
Bibliographic record
Abstract
Currently, municipalities are being tasked to develop improved systematic methodology for allotting their period budgets more appropriately so that their installed buried infrastructure is better utilized and sustained. While capital is typically spent on new infrastructure construction, the maintenance of the present infrastructure must not be neglected. When planning the allocation of investment funds, multiple objectives may exist which are dependent on the constraints, resources available for construction, and the interrelationships and dependencies among all of the alternatives. This makes the task of planning, prioritizing, and allocating funds a complex exercise. In 2000, the City of Edmonton, Alberta initiated a proactive approach to maintaining their wastewater assets by developing a financial outlay model called Proactive Rehabilitative Sewer Infrastructure Management (PRISM). PRISM uses linear programming to optimize allocation of funding for the local sewer network based on deterioration predictive modeling. A more robust version of PRISM was developed in 2002 and has been implemented into the City's asset planning strategy. This paper discusses the City of Edmonton's approach to asset management including assessment of the past five years and describes the framework of PRISM.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".