Defining Resilience for Engineered Systems
Bibliographic record
Abstract
This paper surveys the literature on resilience, provides several definitions of resilience, and proposes a new comprehensive definition for a resilient engineered system, which is: a system that is able to successfully complete its planned mission(s) in the face of disruption(s) (environmental or adversarial), and has capabilities allowing it to successfully complete future missions with evolving threats. This definition captures the subtle differences between resilience and a resilient engineered system. We further examine the terminology associated with resilience to understand the various resilient time-frames and use the terminology to propose a resilience cycle, which differentiates mission resilience (short term) and platform resilience (long term). We then provide insight into various resilience evaluation methodologies and discuss how understanding the full scope of resilience enable designers to better incorporate resilience into system design, decision makers to consider resilient trade-offs in their assessment, and operators to better manage their systems. A resilient engineered system can lead to improved performance, reduced life-cycle costs, increased value, and extended service life for engineered systems.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".