Streamlining System Assurance Program Objectives and Compliance With Both European and American Standards by Early Integration
Bibliographic record
Abstract
Abstract The extent of System Assurance (SA) program complexity changes from one transit project to another, considering the fact that the levels and the types of regulation of safety and security requirements for public transit systems varies significantly in different jurisdictions, states, and countries, depending on the public’s perception of the acceptable (tolerable) risks in using a technology. This paper discusses some of the key SA challenges in new urban rail transit systems being built in Toronto, Ontario, Canada, specifically in terms of the system safety and security assurance compliance with European and American standards, and also explains steps and approaches to help circumvent unnecessary burdens that would negatively impact the cost, the schedule and the effectiveness of the system safety and security assurance programs. A key step in streamlining the SA program in leniently regulated environments, that transit safety and security authorities primarily focus on establishing a “process”, without defining “measurable goals”, is early engagement of the SA program developers with the stakeholders to reach agreement on what constitutes a safe-enough and secure-enough public transit system for passenger service, at the beginning of project, perhaps prior or during the conceptual design phase.
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 | 0.062 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".