Assessing the Usefulness of Assurance Cases: Experience With the Large Hadron Collider
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Assurance cases (ACs) are structured arguments designed to show that a system is sufficiently reliable to function properly in its operational environment. They are mandated by safety standards and are largely used in industry to support risk management for systems; however, ACs often contain proprietary information and are not publicly available. Therefore, the benefits of AC development are usually not rigorously documented, measured, or assessed. In this paper, we empirically evaluate the effectiveness of using ACs to show that a system is reliable using a case study over the CERN Large Hadron Collider (LHC) Machine Protection System (MPS). We used open‐source documentation to create an AC over the MPS and used the Eliminative Argumentation (EA) methodology for its development. The development involved four authors with considerable experience in AC development, three of whom work for Critical System Labs, a small enterprise specializing in ACs. Our findings show that (a) the cost and time required to develop our AC is negligible compared to the effort needed to develop the system, and (b) EA helped identify defeaters (i.e., doubts in the system's reliability) that were not detailed in the documentation used for creation of the AC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it