Safety status: An innovative concept for maintaining the safety integrity level of operational safety systems
Bibliographic record
Abstract
The safety of industrial sites or large research facilities such as CERN (European Organization for Nuclear Research) is obtained by the combined actions of physical security, cyber-security and functional safety that jointly contribute to risk reduction.To deal with functional safety, the IEC 61508-61511-61513 standards are used for regulating design, development and maintenance of the Safety Instrumented Systems (SIS) that implement Safety Instrumented Functions.The SIF's performance is characterized by its Safety Integrity Level (SIL) determined through a risk analysis and conditioned by parameters related to the design, the staff's skills involved in its development, operation and maintenance or the compliance with repairing times.Heads of facility and SIS responsible persons have to continuously master the SIL performance.This is a responsible act to guarantee the risks reduction barriers efficiencies, considering criteria such as periodic testing, spare parts availability, components obsolescence, by-passes, changes control, system real-time status, physical and cyber protections.The Safety Status software imagined jointly by CERN and ASSYSTEM inherits both the CERN experience on the specification and operation of safety systems adapted to the specific risks of particle accelerators and experiments and the know-how developed by ASSYSTEM as a major player in the engineering of nuclear installations, particularly in the field of design and maintenance of critical security and control systems.Safety Status establishes a functional safety dashboard of each system updated automatically or manually with data coming from relevant sources such as maintenance management, document management or the SIS itself.Through a friendly interface, it displays an overview of all the useful information that illustrates the health of the SIS and the integrity of its safety functions.After introducing the concept, the paper presents the methodology, the main features of the software, and the experience feedback gained by its implementation on the CERN MEDICIS facility.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".