Calgary's CTrain: Effective Capital Utilization
Bibliographic record
Abstract
The City of Calgary, a rapidly growing Canadian city of nearly 1 million people, has developed a very effective and efficient public transit system with three light rail transit (LRT) lines forming its backbone. Today, Calgary Transit carries nearly 500,000 daily passengers and nearly half of these customers use LRT for all or part of their journey. In the 1960s, foresight and planning by city leaders identified the need for a high-capacity transit system to reduce the impact of building roads. Although a decision to build LRT was not made until 1976, transit corridors were reserved for some form of high-capacity transit lines as major roads were planned and new communities were being built. After considerable study, LRT was selected as having the greatest potential of attracting users by providing a rapid, reliable, and comfortable trip. LRT also offered lower operating costs and the ability to encourage development that would support transit use. Today, Calgary’s LRT has the highest ridership (both in total and on a per-capita basis) of any North American system. This success has been achieved with a modest level of investment in comparison to costs of other recent LRT systems. Capital costs have been minimized and the effectiveness of the LRT mode has been optimized. This paper explains how Calgary has realized these achievements and become a leader in the transit industry.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".