Toronto's Tunnel Solution (Available in the Geoenvironmental Special Section only)
Bibliographic record
Abstract
The Toronto Transit Commission (TTC), one of the largest public transportation systems in North America, carries more than 1,000,000 commuters a day. The TTC’s tunnels had not received a major restoration of any kind since their construction, but over the years, water infiltration into the tunnels had contributed to delays and safety concerns. The leakage also contributed to a large range of problems, such as extensive concrete and steel deterioration, an accelerated life cycle for the rail and rail fastening systems, deterioration and malfunction of electrical systems and their components, and decay of the structure itself. In view of the great need for structural maintenance, the TCC made tunnel leak remediation a primary objective. The engineers decided that a state-of-the-art acrylamide grouting program would bring an end to the water infiltration problems. Acrylamide was chosen because of its controllable set times, its ability to penetrate the finest and tightest fissures and low-permeability soils, its ability to absorb surrounding water and encapsulate it in the final gel, and its low initial viscosity. The engineers also found that they could accurately predict the volumes of the acrylamide to be used at each location, allowing a precise quantity of material that can be batched on a given shift. The results attained to date have been very successful, and the program is expected to expand in the years to come to include a second grouting work car and a larger crew.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.687 | 0.361 |
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".