Bibliographic record
Abstract
A second train incident occurs when pedestrians assume that they can safely traverse an at-grade road-railway crossing, subsequent to the departure of a train, only to be met by a second train in the crossing area. A second train warning system is designed to reduce the risk of collision resulting from this situation. This study was initiated in 1998 to review the benefit and means of deployment of second train warning (STW) systems in Canada. An industry scan of train warning systems identified two active STW systems. No commercially available STW systems were identified. A functional specification for STW systems was developed. An assessment of nine candidate sites was undertaken, resulting in a recommendation for the pilot test STW site at the O’Brien Avenue crossing in Ville Saint-Laurent, Quebec. The pilot project installation consisted of static STW signs and flashing beacons at the O’Brien Avenue crossing. The results of the “before” and “after” observations demonstrated that the STW system resulted in more than a 64% decrease in total violations, which appears to be consistent with those achieved at other STW pilot program locations. It was concluded that STW systems should be pursued at sites with a high risk of second train incidents/collisions. A qualitative model was developed to rank the at-grade crossings having a potential for second train collisions, with minimal data collection efforts. The results of the qualitative screening would be used to establish a short-list of sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".