MétaCan
Menu
Back to cohort
Record W2314819773 · doi:10.1061/9780784413234.010

Building Telecommunication System Resilience—Lessons from Past Earthquakes

2013· article· en· W2314819773 on OpenAlexaff
Alex K. Tang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsResilience (materials science)TelecommunicationsBusiness continuityDamagesTelecommunications serviceComputer securityPreparednessComputer scienceService (business)TelephonyCommunications systemPublic switched telephone networkTelecommunications networkEmergency managementThe InternetBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.228
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicInfrastructure Resilience and Vulnerability AnalysisFrench-language works237,207