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Record W4321995222 · doi:10.5194/egusphere-egu23-9660

Modeling the Impact of Geomagnetically Induced Currents on Electrified Railway Signalling Systems in the United Kingdom

2023· preprint· en· W4321995222 on OpenAlexaff
J. A. Wild, Cameron Patterson, D. H. Boteler

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsNatural Resources Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsGeomagnetically induced currentEarth's magnetic fieldStormLine (geometry)GroundSpace weatherGeomagnetic stormElectric power systemCurrent (fluid)Interference (communication)IonosphereMeteorologyPower (physics)Electrical engineeringPhysicsEngineeringGeophysicsMagnetic fieldMathematics

Abstract

fetched live from OpenAlex

Studies of space weather impacts on ground-based infrastructure have largely focused on power networks and pipelines, but railway signalling systems are also affected, with misoperations observed in several countries. This paper advances recent theoretical work on geomagnetically induced currents in railway signalling systems by modeling realistic railway lines with parameters from current industrial standards. Focusing on two example lines in the United Kingdom with different locations and orientation, a range of uniform electric fields are simulated along each modelled line. The results show that misoperations could be caused by geomagnetic interference at disturbance levels expected to recur over timescales of several decades. We also demonstrate that the UK estimate for the geoelectric field induced by a 1 in 100-year extreme storm would be strong enough to cause widespread signal misoperations in both lines studied.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.296
Teacher spread0.214 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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