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Record W4327558521 · doi:10.1029/2022sw003385

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

2023· article· en· W4327558521 on OpenAlexaff
Cameron Patterson, J. A. Wild, D. H. Boteler

Bibliographic record

VenueSpace Weather · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsNatural Resources Canada
FundersScience and Technology Facilities CouncilNatural Environment Research CouncilSight Research UK
KeywordsGeomagnetically induced currentEarth's magnetic fieldGeomagnetic stormSpace weatherGroundElectric power systemLine (geometry)MeteorologyIonospherePower (physics)EngineeringElectrical engineeringGeophysicsGeologyPhysicsMagnetic field

Abstract

fetched live from OpenAlex

Abstract Studies of space weather impacts on ground‐based infrastructure have been largely focused on power networks and pipelines, but railway signaling systems are also affected, with misoperations observed in several countries. This paper advances recent theoretical work on geomagnetically induced currents in railway signaling 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 modeled 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.172
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.038
GPT teacher head0.293
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2023
Admission routes1
Has abstractyes

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