Urban Services on Regional Rail: Local and Network-Based Methodologies for Evaluating New Stations in Toronto
Bibliographic record
Abstract
The Greater Toronto and Hamilton Area’s regional GO Rail network is undergoing a substantive upgrade to provide a two-way, all-day, electrified, 15-min service. These investments have led to discussions about the nature of the service and calls to add new stations, particularly within the City of Toronto. This paper explores the methodology used to evaluate specific new station locations in the City of Toronto and beyond, along with a concurrent analysis of the impacts of a package of new stations on the network performance and hierarchy. Both assessments used a multi-faceted business case framework. Travel-time impacts, land use, costs, feasibility, and other factors influenced the recommendation of sites intended to optimize the substantial investment in network infrastructure, and address city-building objectives like encouraging development and providing access to underserved communities. Network analysis points to new stations within the city shifting the traditional three-tier network hierarchy comprised of regional rail, urban rapid transit, and local transit to a hybrid network in which regional rail serves some intermediate functions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".