Bibliographic record
Abstract
The Réseau Express Métropolitain (REM) is an electric and fully automated, light-rail transit network designed to facilitate mobility across the Greater Montreal Region (Canada). This new transit network will be linking downtown Montreal, South Shore, West Island, North Shore and the Airport. Spanning over 67 kilometers once completed, the REM will be one of the largest automated transportation systems in the world after Singapore, Dubai and Vancouver. The REM system will connect to existing bus networks, commuter trains and three lines of the Montréal metro (subway). To deliver this major design-build project, several underground works are undertaken. One of the major and challenging underground works of this project is the Édouard-Montpetit Station. The Édouard-Montpetit Station is an interchange station that will connect the deeply sitting REM tracks laid within the century-old existing Mont-Royal tunnel to the existing metro station of the same name located closer to the surface. The tracks being located at approximately 70m below surface, the Édouard-Montpetit station will be the deepest in Canada. This paper will present various components of the design and construction of this deep underground station and the numerous challenges and solutions put forth for its successful completion. It discusses a case of a very deep transit station located in a dense urban area and excavated in hard rock on top of an existing 100-year-old tunnel. The details of design and construction as well as their specific challenges are explained, and the solutions selected to overcome these difficulties are described. Two thin layers of shotcrete combined with permanent rock bolts and a layer of spray-on waterproofing membrane are used for both initial and final liner of the station shafts, tunnels, and caverns resulting in an optimized design and construction process.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".