Bibliographic record
Abstract
From a small 7.4 km line with 12 stations in 1954 to full network of four lines, 69 stations and close to 70 km of track length today, the Toronto metro has become an integral part of the city's transportation system; in 2008, it carried more than 200 million passengers (number of fares collected). The goal of this paper is to offer review of the Toronto metro by looking at its history, demand, and performance. First, the authors find that ridership and operations have increased relatively similarly from 1967 to 1990 at about 4.3%; after a decrease in the 1990's, ridership now increases by 2.59% annually on average. Nevertheless, despite this increase in ridership, transit mode share has remained around 22% in the past 25 years due to a strong growth in population. Demand seems to be more acute at stations located within the Central Business District and at stations located close to neighboring municipalities, which is reflected by the fact 69% of trips are home-work/school trips. Compared to its North-American peers, and despite a relatively small track length, the Toronto metro is performing quite well when looking at various characteristics and indicators. Overall, the Toronto seems to have performed well to date; nevertheless it will likely need to be expanded significantly in the new future to accommodate the forecasted substantial growth in population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".