Bibliographic record
Abstract
This paper covers the importance of addressing utility conflicts early in the Ontario Line project. With over 1,500 identified utility conflicts and 186 unique relocation projects, the scale of the task is immense. The presentation will delve into the process of identifying utility conflicts on the project, offering insights that can benefit similar transit projects across North America. It will emphasize the Subsurface Utility Engineering (SUE) procedure and the meticulous steps involved in accurately pinpointing utility information and locations. This paper will detail how the information obtained from SUE is integrated with the project scope to identify and document utility conflicts. Furthermore, the presentation will highlight the collaborative efforts between the utility design team, third-party utility companies, and other project disciplines to proactively address utility relocations, including the crucial step of “space proofing” for utility relocations and ensuring the relocation of dry utilities before commencing other project works. The paper will pinpoint a specific conflict from the project and use it as an example for the overall processes and challenges. The Paper will then demonstrate the actions for addressing the conflict through each phase of the relocation process.
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.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".