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.
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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 | 0.000 | 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".