Bibliographic record
Abstract
Condition assessment of subway stations is a major issue facing public transit authorities worldwide. The Société de Transport de Montreal (STM) requires a rehabilitation budget of CAD 643.6 million (2006–2010) for its aged stations. The STM and most transit authorities lack planning strategies that reflect this increase due to deficiency of condition assessment models and scarcity of existing models. The research presented in this paper assists in developing a condition assessment model (subway station diagnosis index). The model identifies and evaluates the weights of different functional (structural/architectural, electrical, mechanical, and security/communication functions) condition criteria for subway stations using the analytical hierarchy process. It also utilizes both the Preference Ranking Organization METHod of Enrichment Evaluation and the Multiattribute Utility Theory to determine the station diagnosis index (SDI). Data are collected from experts through questionnaires and interviews. A case study in the STM subway stations network is performed. Data analysis shows that structural and security criteria are the most important (36.1 and 27.3%, respectively). The STM stations are found deficient, with an average SDI of 4.4 out of 10. This research is relevant to industry practitioners and researchers, since it provides a condition assessment tool and a unified universal scale for subway stations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".