Case Study—Railway Embankment Widening for CN Rail and GO Transit
Bibliographic record
Abstract
GO Transit, The Greater Toronto Area's commuter rail/bus system is currently undergoing an expansion in service. Part of this expansion is an improvement in the limited rail service between Toronto and Hamilton Ontario. In some cases, in order to meet the increased commuter volume, it is necessary to add additional tracks to accommodate the existing and future rail traffic. This is the case on the Lakeshore West Line where a third track was only the way to meet demand. In the Lorne Park area of Mississauga, existing conditions did not provide sufficient room for a number of rail embankments to be widened without the use of an earth retention system. Numerous retaining wall and over-steepened slope options were considered for seven distinct locations. The ultimate solution was a prototype Mechanically Stabilized Earth (MSE) retaining wall that combined a steel fascia with geosynthetic reinforcement. This case study will examine the various aspects of this project from the initial stages of design, through the various solution options, to the development of the final solution. With a restricted time schedule and construction constraints, the paper will also detail how those major obstacles were successfully overcome in the completion of this project.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".