Bibliographic record
Abstract
In order to evaluate the effects of the proposed changes to the Railway Safety Act, the City of Vancouver (City) asked MMM Group to use the Rupert Street grade crossing to evaluate the effects and challenges of meeting these new Grade Crossing Regulations (Regulations) at all City grade crossings. The proposed Grade Crossing Regulations require that Railway Companies and Road Authorities share information about public grade crossings within five years of the coming into force of the Regulations. In addition, Railway Companies and Road Authorities would be responsible for ensuring that all grade crossings meet the Basic Requirements prescribed in the Regulations within five years of the coming into force of the Regulations. Given that there are more than 14,000 public grade crossings across Canada, this represents a significant investment in time, expertise, and money. Rupert Street is a four-lane secondary arterial that carries more than 28,500 vehicles per day. Two tracks of CN Rail’s New Westminster Subdivision cross Rupert Street at an active crossing equipped with flashing lights, bells, and gates (FLBG). About six trains use this crossing on a typical day. The high level of vehicle activity at this crossing is exacerbated by the pedestrians, cyclists and transit passengers using the adjacent Rupert SkyTrain Station and BC Parkway multi-use trail. Based on the Rupert Street findings, MMM advised the City as to what information is needed, and how best to collect it, for Vancouver’s more than 100 grade crossings. MMM also provided cost estimates for gathering the various pieces of information for a typical Vancouver grade crossing to assist with budget planning. This paper will: identify the types of information (i.e. information on the 40 data fields) that need to be collected, as well as the methods and/or sources for capturing the required location and technical data; summarize the road-rail parameters and information that needs to be collected and shared between Responsible Authorities, in this instance the City of Vancouver and CN Rail; and present order of magnitude cost estimates for completing the data collection and processing that will assist the Road Authorities with budget planning. As a result of the information that MMM provided about Grade Crossing Information, how best to collect it, and the associated costs; Road Authorities are in a position to proactively address changes to Canada’s Grade Crossing Regulations.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".