Evaluating the use of Subsurface Utility Engineering in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it