Bibliographic record
Abstract
Rolling contact fatigue (RCF) is a pervasive and insidious problem on all types of railway systems. Although it is a dominant cause of maintenance and replacements on heavy haul rail lines, it is also a significant economic and safety challenge for commuter and metro lines. It is a subject of intense research around the globe, with strong academic research being undertaken in Europe particularly, with more practical work being performed in Australia, South Africa, and North America. The safety implications of RCF include being responsible for about 100 FRA reportable derailments annually in North America. The poster child for hazardous RCF is the Hatfield derailment in the United Kingdom, which incurred four deaths, 39 injuries, and economic fallout easily exceeding GBP1 billion, dismemberment of the railway authority and manslaughter charges against several railway officials. The economic implications of RCF to the North America railway industry for rail replacement alone amounts to over USD300 million annually, with costs of inspection and derailments, as well as damage to track and rolling stock, and derailment costs further increasing that number. Of the USD100+ million dollars spent annually on rail grinding in North America, at least ≥30 percent can be attributed to RCF. A review of the types or RCF defects on wheels and rails, causal mechanisms and monitoring and maintenance practices has been undertaken for the purpose of identifying gaps and the most pressing areas for research and development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".