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Record W4286009377 · doi:10.32866/001c.37221

Who is the Weakest Link? A Network Vulnerability Analysis Using a Congested Transport Assignment

2022· article· en· W4286009377 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFindings · 2022
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsTransport Canada
Fundersnot available
KeywordsBetweenness centralityLink (geometry)Vulnerability (computing)Computer scienceCriticalityCentralityVulnerability assessmentComputer networkTransit (satellite)Transport engineeringComputer securityPublic transportEngineeringMathematics

Abstract

fetched live from OpenAlex

We propose a user-equilibrium congested transit assignment model for a full-scan network vulnerability analysis by relying on the computations of network science indicators for infrastructure and service graphs. Model output include line-specific passenger loads. Furthermore, we propose a link criticality indicator for measuring the spatial extent of spill-over effects of link closures. In the case of the Amsterdam a disruption on the most critical link in the peak period induces 1,000 passenger-hours loss and a disruption spill overs on average to more than seven other links. Betweenness centrality and passenger loads do not offer good proxies for assessing link criticality.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.075
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.234
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