Use of Subsurface Utility Engineering Data for Multiple Disciplines on Mega Projects
Bibliographic record
Abstract
It is well recognized that utility conflicts pose one of the top risks on complex infrastructure projects. Subsurface utility engineering (SUE) is often used as part of the design process to provide utility coordinators and design engineers with valuable data needed to complete project designs. That same SUE data can also be used by other disciplines on the project. The geotechnical team is one of those disciplines that can use SUE data to improve the efficiency and effectiveness of the geotechnical programs. This paper will explore the use of SUE on large infrastructure projects and identify how it can be integrated into other disciplines focusing on the geotechnical inspection program and generation of the geotechnical baseline report (GBR). Ontario has been a leader in the use of private public partnership (PPP) projects and has made use the use SUE as standard practice at the preliminary design stage of most large projects funded by the government. Megaproject, the Hamilton LRT will be used as an example of how SUE data was used on a large-scale urban PPP project.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".