A Framework for Integrating Reliability, Robustness, Resilience, and Vulnerability to Assess System Adaptivity
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 The growing effort to improve a mechanical system’s performance with a sustainable perspective has created more complexity due to the need for additional technological subsystems. Increased complexity could result in new failure modes for systems making performance assessment more challenging. Therefore, it is essential to develop frameworks to assess performance based on a broader approach beyond single indicators. However, when considering reliability, resilience, robustness, and vulnerability (3RV) concepts as single mathematical-based models for assessing a system’s performance, designers are confronted by similarities between these concepts. In this regard, integrating these four concepts and developing a comprehensive variable (herein called system adaptivity) could better unify 3RV as a single objective function. Consequently, this study presents independent definitions for each concept and identifies common aspects and interrelationships between them. Finally, a system adaptivity objective function will be defined quantitatively by evaluating identified characteristics and internal and external relations for each concept in the previous step. This new prospect could represent a system’s adaptivity as an integrated framework towards different defined failure scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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