Implementation of subsurface utility engineering in Ontario: cases and a cost model
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
This paper investigates a relatively new engineering service that is being introduced in Ontario: subsurface utility engineering (SUE). This service combines civil engineering, surveying, geophysics, and nondestructive excavation for the accurate mapping of underground utilities. This paper presents the results of a one-year study that investigated the use of SUE on large infrastructure projects in Ontario. The study involved performing a detailed cost analysis of nine successful SUE projects, four of which are presented in this paper. Potential cost savings were estimated for each case study and all indicated that SUE has a positive return on investment. In addition, two industry-wide surveys were conducted to investigate the effects of inaccurate utility information on projects. Results indicate that inaccurate utility information has a significant impact on project cost, schedule, and damage to existing utilities. Using the results of the case study analysis and the survey, a generic cost model for SUE was developed that relates project specific characteristics to costs that could be incurred because of inaccurate utility information. This investigation provides valuable insight to the application of a relatively new process in Canada following successful results in the United States.
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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 it