RZDD: Risk Zone-Diversified Network Design for Disaster 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
With the growing need for a robust network backbone to ensure uninterrupted connectivity in the face of large-scale natural disasters, we introduce the Risk Zone-Diversified Network Design (RZDD) problem. This problem requires diverse paths between source-destination pairs to be risk zone-disjoint, preventing any single disaster from disrupting overall network connectivity. Unlike previous research, we propose an innovative cost framework that considers geographically overlapping links and long-term maintenance costs, providing a comprehensive approach to cost analysis. We prove the intractability of the RZDD problem and present the Risk Zone-Diversified Network Design Algorithm (RZDD-Algorithm). In small-scale networks with a single source-destination pair, our algorithm achieves optimal outcomes. Comparative analysis shows that our method reduces costs by an average of 24% compared to an SRLG algorithm that does not consider the preference of geographically overlapping links. For multiple pairs, our approach maintains a gap ratio within 4% and 7% of optimal solutions. Furthermore, experimental evaluations on large networks demonstrate reductions of 26% and 31% compared to the SRLG baseline for single pairs. We also showcase the efficiency of our method in designing large-scale networks with multiple pairs.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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