The application of the CE regulation 402/13 and the quantitative evaluation of risk to the Italian Railway ‘SSC’ (supporting system for the driver) control command system
Bibliographic record
Abstract
Proper hazard analysis and risk evaluation management are the main steps to define the safety requirements of a railway control command system aiming to protect trains from their physical constraints, the limits of the infrastructure they have to run on and the traffic constraints as they share the same infrastructure with other vehicles.After a short overview of the Italian national railway control command systems, the goal of this paper is to describe the approach adopted for providing the hazard analysis to the protection system named SSC (Supporting System for the Driver -Sistema di Supporto alla Condotta) with a special focus on the risk assessment phase where the quantitative evaluation of risk at system level was performed including human factor (particularly driver error).The applied methodology adheres to the European Commission Regulation 402/13 on the common safety method for risk evaluation and assessment, and it is in line with the CENELEC standards EN50126 and EN50129 valid for safety-related electronic systems for railway signalling and communication applications.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".