The North American Light Rail Experience: Insights for Hamilton
Bibliographic record
Abstract
This report provides a high level overview of the North American Light Rail Experience with the goal of providing insights for Hamilton, Ontario. The report considers the examples of 30 light rail systems constructed in North America, providing a synopsis of each and deriving lessons relevant for light rail transit (LRT) planning in Hamilton. In Canada, these cities are represented by Calgary, Edmonton, and Toronto. The main body of this report is separated into three chapters. Chapter 2 reviews the general North American literature on light rail with an emphasis on recipes for success. Dimensions of interest include useful policy perspectives and specific policy tools with an emphasis on transit-oriented development (TOD). Other important aspects are a review of specific quantitative outcomes of past light rail projects and an examination of potential light rail pre-requisites that can be important. Chapter 3 reviews four real-world cases in cities where light rail has been implemented and offers an opportunity to consider Chapter 2 insights in a more applied context. Finally, Chapter 4 offers some concluding marks with some assessment of the implications for light rail in Hamilton. Note that Appendix A in particular is an integral part of this document as it provides brief overviews of all the LRT cities in North America and in this way complements Chapter 3.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".