Back-Wall Grouting using Acrylamide Successfully used in Toronto's Subway
Bibliographic record
Abstract
Plagued by leakage problems since the time they were constructed, Toronto's underground network of subway tunnels and structures have undergone a major leak remediation program. The spring of 2004 marks the seventh year of a very successfully implemented solution grouting program. This project has been the largest on-going solution grouting project in North America for the last five years. The Toronto Transit Commission's water problems were brought under control with the use of a "back-wall" grouting technique that took into account the multiple phase, multiple stage grouting operations that were anticipated and required to successfully shut off the leakage problems. The water infiltration problems was causing accelerated aging of the rail and rail fastening systems, deterioration and malfunction of electrical systems and their components and decay of the structure itself as well as causing both service delays and concerns for passenger safety. All the work had to be performed during the nightly two-hour maintenance working window without impact to customer service. This paper hopes to provide some helpful insight on the key aspects of this specific sealing system engineered and designed to solve Toronto's subway tunnel and station leakage problems. This successful design continues to be the leak remediation solution of choice for this public transportation authority.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".