Bibliographic record
Abstract
Réseau Express Métropolitain (REM) is the new light rail transit network of Montreal, Canada. This electric and fully automated LRT network will facilitate mobility across the Greater Montreal Region by providing 67 km of twin tracks and 26 stations. This new LRT network uses existing Mont-Royal Tunnel (MRT) to connects Downtown Montreal to the north side of the Island of Montreal by passing through Mount Royal mountain. In addition, two of the project stations will be built inside this tunnel by enlarging it from a double-track tunnel to side-platform station at the location of these stations. The Mont-Royal Tunnel (MRT) is a century-old railway double track horseshoe tunnel. The tunnel is approximately 5 km long, 8.8 m wide and 5.5 m high, with a constant 0.6% grade. In order to use MRT in a modern transit system, the tunnel needed to be inspected, rehabilitated and be compliant to the recent safety standards. This paper presents a summary of history and characteristics of the existing tunnel plus the implemented steps to upgrade the tunnel. It includes the details of the structural inspection and rehabilitation methods of the tunnel. Further, this paper summarizes the procedures for the space proofing, construction of the separation wall, and the tunnel enlargement. It also describes parts of complications encountered during the constructions and solutions implemented to tackle these issues.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".