Building Telecommunication System Resilience—Lessons from Past Earthquakes
Bibliographic record
Abstract
From a system perspective, telecommunication has been performing very well in a number of major post-disaster environments. The above-normal demand for circuits right after a medium-to-large earthquake invariably created a perception of system failure. The fact is that the telephone system is not built to accommodate everybody making a call at the same time. Even if there are no physical damages to the telecommunication system, the symptom of telephone not working will exist. This paper will examine the current level of telecommunication system resilience based on past earthquake performance investigations. Reviews of preparedness and network upgrades before and after earthquakes will hopefully provide us with a path to develop a more resilient system. This includes the Internet network system, which is a key part of today's business environment. Performance of major elements of the telecommunication system will be presented. Post-disaster system emergency policy and recovery planning will be suggested for discussion purposes. The expectation is to generate interest in telecommunication service providers to develop standards or practices that will lead us to a more resilient telecommunication system. Lifeline interdependence will also be discussed. All lifelines have to co-exist as a unit in order to provide the community with security and business continuity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".