Using Subsurface Utility Engineering (SUE) to Reduce Delays and Disruptions on City of Toronto's Yonge Street Project
Bibliographic record
Abstract
As more and more municipalities look towards replacing aging infrastructure along key transportation corridors, one common goal is minimizing the interruption to traffic and business along those critical routes. This paper reviews the City of Toronto's use of Subsurface Utility Engineering (SUE) services to manage this important issue for their Yonge Street Redevelopment Project. One element is the replacement of the existing watermains along Yonge Street from Eglinton Ave to Lawrence Ave. In addition to the watermains, there are plans for sewer chamber rehabilitation, sewer lining, boulevard reconstruction and road resurfacing within the project area. In order to minimize disruption along this critical corridor the City looked at methods of gathering utility information that could be used to help minimize or eliminate potential delays during construction. The City decided to complete a Subsurface Utility Engineering (SUE) investigation, in accordance with the CI/ASCE 38-02: Standard Guidelines for the Collection and Depiction of Existing Subsurface Utility Data. The authors will analyze how the scope of the investigation was developed to ensure the efficient gathering of the data. They will also discuss the techniques used for the investigation, and comment on the benefits and limitations of each technique based on the site specific conditions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".