Considering User Needs Versus Environmental Constraints to Favour Intermodality
Bibliographic record
Abstract
The mission of the Agence metropolitaine de transport (AMT) is to expand public transit services in order to improve the efficiency of individual travel in the metropolitan area of Montreal. The AMT participates actively in promoting intermodality between the different transit corporations and the various transportation modes of its territory, which includes over 83 municipalities. In a bid to form part of a comprehensive national and North American vision, the AMT bases its actions plan on a triennial base, with four major orientations: access to communications, infrastructure, rolling stock and the human factor (training and sensitization). The objective is to prioritize actions to make a regular transit system accessible, while accounting for the four basic orientations and various environmental constraints, particularly as related to the railway context. For this purpose, the AMT has produced a comprehensive picture of the greater metropolitan region, including several sociodemographic indicators, with the aim of focusing reflection on solutions to improve access to the transit system. Access to the regular transit system is the first link in the chain to allow social, educational and professional integration of people with limited mobility. Defining coherent standards within the different environmental constraints, in relation to the importance of intermodality and the users’ needs, is an exercise requiring an integrated, structured, concerted approach, as shown in the action plan. The recent purchase of new, accessible train cars genuinely reflects this approach. Representatives of different paratransit user groups joined the AMT team on a trip to Plattsburgh, in upstate New York, to view the cars and make their recommendations with the goal of meeting users’ needs. Their concrete participation on that U.S. trip greatly influenced certain changes subsequently made to the cars, particularly the fact of increasing the number of places available for wheelchair users. These same cars will serve certain train lines running to Central Station, adjacent to the Downtown Terminus, which is currently undergoing work with the aim of making it accessible. All these actions coordinated around this key modal transfer point on the Island of Montreal let us make the trip easier for all commuters, including people with reduced mobility and handicapped persons.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".