Process Safety Approach to Identify Opportunities for Enhancing Rail Transport Safety in Canada
Bibliographic record
Abstract
The amount of dangerous goods (DG) transported by rail within Canada has increased by an average of approximately 25% since 2004, with a 42.5% increase in transported fuels and chemicals between 2011 and 2017. Further, movement of DG by rail is forecasted to continue increasing. Sustainable growth in the transport of dangerous goods (TDG) by rail will require acceptable safety levels. This study provides insight into key occurrence types for TDG and their causes, to better focus on risk control strategies, including measurement and control of leading and lagging safety indicators. This work also reviews current safety performance and Canadian railway incident occurrence databases. The results of the analyses suggested that the performance against lagging indicators currently being reported is adequate, including derailments and collisions (main and nonmain track), serious injuries (including fatalities), DG leakers, and releases. Furthermore, a list of the rail accidents with the greatest number of fatalities was used to calculate a crude estimate of societal risk associated with rail transportation. According to UK Health and Safety Executive (HSE) recommendations, this analysis indicated that the estimated rail transport risks would be considered acceptable when assessed at a milepost scale. However, there are opportunities for further enhancing safety reporting, management, and performance.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".