Modeling “Wrong Side” Failures Caused by Geomagnetically Induced Currents in Electrified Railway Signaling Systems in the UK
Bibliographic record
Abstract
Abstract The majority of studies into space weather impacts on ground‐based systems focus on power supply networks and oil and gas pipelines. The effects on railway signaling infrastructure remain a sparsely covered aspect even though these systems are known to have experienced adverse effects in the past as a result of geomagnetic activity. This study extends recent modeling of geomagnetic effects on DC signaling for AC‐electrified railways in the UK that analyzed “right side” failures in which green signals are turned to red. The extended model reported here allows the study of “wrong side” failures where red signals are turned green: a failure mode that is potentially more dangerous. Railway lines using track circuit signaling, like those modeled in this study, are separated into a number of individual blocks. This study shows that a relay is most susceptible to “wrong side” failure when a train is at the end of a track circuit block. Assuming that each train is positioned at the end of the block it is occupying, the results show that the geoelectric field threshold at which “wrong side” failures can occur is lower than for “right side” failures. This misoperation field level occurs on a timescale of once every 10 or 20 years. We also show that the estimated electric field caused by a 1‐in‐100 years event could cause a significant number of “wrong side” failures at multiple points along the railway lines studied, although this depends on the number of trains on the line at that time.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".