Lifeline System Interdependencies—Key for Resilience in Practice
Bibliographic record
Abstract
This paper explores the practical role of the interaction between critical lifeline systems that can impact their mutual performance as well as their ability to serve the community, especially in the aftermath of seismic events. In particular, the emergency and recovery planning of individual lifeline systems is discussed in relation to power and telecommunication system resilience as seen from different post-earthquake reconnaissance investigations and future evolution of these increasingly coupled systems. Seismic hardening of lifeline systems is improving either by means of enforcing standards, material advances, installation practice and quality control; but, these efforts are concentrated on individual components of individual systems. Hence, a new focus is needed that includes interconnections between components and coupling across systems, while distinguishing functional and management layers for enhanced tractability. Advances in telecommunication technologies make lifeline systems rely more on automated system control and monitoring during normal and abnormal operation regimens. Therefore, communication systems are critical for uninterrupted service and coordinated restoration across modern lifeline systems after a major disaster — all essential to community resilience. An example of the new methods to assess the observability and potential management of coupled networks is thus presented, while also highlighting research and implementation tasks for resilience based on practical operational and post-disaster observations on interdependencies in the last decade.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".