A discussion on the use of Eliminative Argumentation (EA) to identify Key Performance Indicators (KPIs) for the CERN LHC Machine Protection System
Bibliographic record
Abstract
Key Performance Indicators (KPIs) and Safety Performance Indicators (SPIs) form an integral part of the Safety Management System (SMS) for a selected system.They provide a key insight into the system's safety performance and risk management, and enable data-driven decision-making.A KPI for a system is defined as "a quantifiable measure used to evaluate the success of an organization, employee, etc. in meeting objectives for performance".The KPIs discussed within this paper denote a measure of success/performance of the relevant identified sub-systems.Integration of KPIs and SPIs serves as a method of performance and safety evaluation of the systems they are associated with.KPIs can be used to estimate the safety performance of a system, as well as to support the safety case and ensure that it remains "fit for purpose" and "live".The paper also discusses how KPIs can be grouped into "leading" and "lagging" indicators.A leading indicator is one that tracks the occurrence of events that, while not themselves harmful, are expected to precede, or indicate the potential for, more harmful events.A lagging indicator is one that tracks the occurrence rate of hazards and/or loss events, such as crashes, injuries and fatalities.Leading and lagging indicators have limitations, advantages and disadvantages, which will be discussed further in the paper.Further we also discuss the challenges of accurate data collection to support KPIs.KPIs have a variety of potential uses, such as tracking safety trends over time, measuring system compliance to regulations/legislation, and providing evidence for the system's safety case.This paper will focus on how KPIs can be defined from the safety (assurance) case assessment process.Specifically, this paper demonstrates the use of Eliminative Argumentation (EA) to define the potential hazards associated with the machine protection system at the nuclear research facility CERN.We discuss the evaluation and identification of the KPIs for each of these systems.Further, we show how performance indicators are identified with the EA assessment and the corresponding nodes, whilst demonstrating how the content of this assessment is linked via a "golden thread".We show how they can be analysed post-mortem to ensure that the safety case remains valid and "live" as the system changes.Finally, we discuss how the use of KPIs can benefit the safety case and why ensuring that it remains "live" (fit for purpose) is critical to the continued safe operation of a system.In summary, KPIs play a critical role in keeping a safety case live by providing ongoing monitoring, driving continuous improvement, providing documentation, and establishing accountability for safety performance.By using them effectively, organizations can ensure that safety goals are being met over time.
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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 | 0.031 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".