Bibliographic record
Abstract
Abstract Misoperation of railway signaling during geomagnetic disturbances has occurred in a number of countries. Railway signals are activated by track circuits that detect the presence of a train in a particular rail section, but geomagnetically induced electric fields can interfere with the track circuit operation, causing the wrong signal to be displayed. This paper develops a new model for track circuit operation that includes the induced geoelectric fields produced by geomagnetic field variations. Rails are modeled as transmission lines with series impedance given by the rail resistance and parallel admittance determined by the current leakage from the rail through the ballast to ground. The transmission line model is then converted into an equivalent‐pi circuit for each rail and these are combined with the track circuit power and relay components to form a nodal admittance network for the track circuit. This is used to examine the effect of induced geoelectric fields on track circuit operation. First, an individual track circuit is examined and it is shown how the length, between insulating rail joints, of the individual rails influences the geomagnetic interference voltage produced across the track circuit relay. Then a series of track circuits is considered: A general solution is developed and then the conditions are identified that allow this to be reduced to a simpler solution considering each track circuit individually. This modeling provides insight into the conditions that produce signaling problems and provides the tools to assess the geomagnetic hazard to railway signaling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".