Advanced Technology for the Identification of Stray Current and Measurement of Track Resistance for DC Powered Rail Systems
Bibliographic record
Abstract
Abstract Stray current corrosion that results from the operation of electrified DC transit systems has been an issue for decades, with agencies spending significant sums to address it. Stray currents are known to greatly accelerate the corrosion of metallic infrastructure and can lead to failures if left unchecked. They not only affect the operation of transit agencies, but can also impact external infrastructure, leading to regulatory issues and unforeseen direct and indirect costs. For many electrified railways, the running rails serve as the return path for DC electrical current to the substation. Electrical isolation of the rails is crucial to prevent the current leaking into the adjacent infrastructure. While various solutions exist to measure stray current and track insulation over longer sections of rail, these solutions are limited in their ability to efficiently identify the precise location where the leakage is occurring. This paper describes the recent development of a new technology that significantly reduces the time to locate and address track insulation faults, by identifying the severity and location of stray current along the length of rail. This innovative tool has successfully demonstrated value on multiple operating rail systems and examples from field test campaigns are included to demonstrate this capability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".