Risk-adaptive strategic network protection in disaster scenarios
Bibliographic record
Abstract
The dynamic behaviour of natural disasters and their probabilistic failure pattern indicates a need for a dynamic, risk-based protection approach to reduce the number of disrupted connections in the network. Appropriate traffic protection against a time-varying destructive phenomenon serves to prevent damage before it occurs. In this case, the level of risk for traffic routes should be evaluated and the flow should be rerouted to more reliable paths prior to failure. The high-risk paths can be identified based on appropriate decision parameters in a preventive protection scheme as an effective dynamic probabilistic solution to address large-scale failure scenarios. In this paper, we study the effect of dynamic tuning of decision parameters on network performance and discuss their impact on traffic protection. Furthermore, we develop a self-adapting preventive approach to enhance traffic protection with respect to disaster behaviour and undamaged, operational network resources. The proposed approach dynamically adjusts rerouting decision parameters to provide an appropriate level of protection while the impact domain of the natural disaster expands through the region and increases the risk of failure for network components. Our simulations, conducted in real-world topologies, confirm the feasibility of the proposed approach for traffic protection in large-scale failure scenarios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".