Evaluating the use of Subsurface Utility Engineering in Canada
Bibliographic record
Abstract
The market for Subsurface Utility Engineering (SUE) in Canada is slowly following its U.S. counterpart. In Ontario, the market for SUE has seen rapid growth especially on large-scale municipal projects. This paper presents the results of a 12-month study that investigated the challenges and opportunities facing SUE in Ontario. The study took an in-depth look at 9 large municipal and highway reconstruction projects that utilized SUE to provide an enhanced depiction of buried utilities. Based on this analysis, a cost model for SUE utilization was proposed that takes into account both tangible and intangible benefits. This model was applied to gauge the expected cost savings due to SUE utilization on these 9 projects. All projects showed a positive return-on-investment (ROI) that ranged from $2.05 to $6.59 for every dollar spent on SUE. Although these ROI figures should not be considered universal, they indicate that with careful scoping of SUE services, project risks can be appropriately reduced at reasonable cost. The paper concludes with a set of lessons learned by various project participants from the SUE experience in Ontario.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".