Challenges in Utility Coordination and Implementation of Pavement Degradation Fees
Bibliographic record
Abstract
The City of Calgary (The City) has a road network of nearly 16,000 lane-kilometres with an asset value of about $11 Billion. On an extensive roadway network like this which is further growing it can be very expensive and disruptive to carry out maintenance activities on sections affected by utility cuts. A forensic investigation was conducted to determine the level of impact on the serviceability of pavements in Calgary due to the utility cuts and the findings were presented at TAC Conference in 2014. The study estimated 22 percent as the loss of service life. Based on the findings, The City decided to engage the utility companies, developers and other stakeholders in implementing the pavement degradation fees. In 2015, The City implemented pavement degradation fees to recover costs associated with reduction of service life and any maintenance costs associated with it during its life cycle to bring the road back to the condition prior to the utility cut. While pavement degradation fees is charged on all utility cuts, surface restoration fees has been historically applied to roads with Visual Condition Index (VCI) greater than 7.0 and/or roads less than two years old. This paper presents the implementation process and associated challenges where stakeholders from various quarters were involved. The paper identifies the steps taken to improve the coordination of right-of-way projects between The City, developers, contractors and Utilities.
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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 | 0.033 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".