Bibliographic record
Abstract
The spring of 1997 marked the beginning of a professionally engineered, state-of-the-art remediation grouting program at the Toronto Transit Commission (TTC). A Tunnel Leak Remediation program was developed with the objective of stopping the water infiltration problems that plagued the Toronto subway tunnels since their construction. Water infiltration into the subway tunnels results in service affecting delays as well as concerns relating to the safe operation of a transportation system serving over a million riders per day. Problems include: accelerated aging of the rail and rail fastening systems, deterioration and malfunction of electrical systems and associated components and deterioration of the structure itself. A two-hour nightly working window, in which maintenance activities can be performed, combined with the marginally injectable ground conditions and difficult tunnel structure made achieving positive results especially challenging. An engineered solution grouting injection program using acrylamide technically proved to be the most suitable design for this particular application. The newly hired in-house work force had never previously worked with acrylamide solution grouts and therefore had to be trained and coached in all aspects of the grouting procedures. The acrylamide grouting operation is closely monitored under stringent quality control parameters and is implemented under what is likely one of the highest levels of personal protection ever used for an acrylamide grouting application. Acrylamide was never previously used anywhere on Toronto Transit Commission property, but it has demonstrated to be an invaluable tool in solving difficult water infiltration problems in difficult ground conditions under a limited two hour working window. This paper shares some of the experiences gained to date in this on-going Tunnel Leak Remediation Program at the Toronto Transit Commission.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".