A model of interdependent infrastructure system resilience
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
Infrastructure systems of transportation, water supply, telecommunications, power supply, etc. are not isolated but highly interconnected and mutually coupled. Infrastructure interdependences can increase system vulnerability and produce cascading failures at the regional or national scales. Taking the advantage of network theory structure analysis, this paper models a multilayer infrastructure network of street, water supply, power supply and information infrastructure layers. The infrastructure interdependences are detailed using five basic dependence patterns of basic network elements. Definitions of dynamic cascading failures and recovery mechanisms of infrastructure systems are also established. The main focus of the paper is the introduction of a dynamic measure of infrastructure network resilience capable of addressing infrastructure system, as well as network component (layer), interdependences. The measure is based on infrastructure network performance, proactive infrastructure network resistance capacity and reactive infrastructure network recovery capacity. With three resilience features and corresponding network properties, this paper develops the quantitative measure of dynamic space-time resilience and a resilience simulation model for infrastructure networks. The resilience model is applicable to any type of infrastructure and its application can improve the decision-making processes of infrastructure planning, design and maintenance.
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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 | 0.000 | 0.000 |
| 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