The spatial correlation between track roughness and ground-penetrating radar inferred ballast degradation
Bibliographic record
Abstract
Ballast degradation is considered to be a primary factor that contributes to the development of track roughness, and as such it is important to develop efficient techniques to assess the condition of the ballast. Ground-penetrating radar is one method that has been applied in a variety of railway foundation studies including those attempting to non-destructively assess ballast degradation. However, there has yet to be a large-scale study that attempts to correlate the ground-penetrating radar-based estimates of ballast degradation with the observed track roughness. This study investigates this correlation along a 335 km-long heavy-haul railway subdivision in Alberta, Canada. Track roughness is quantified from repeated track alignment and surface measurements spanning 15 months prior to the ground-penetrating radar data acquisition. Three sets of 400 MHz ground-penetrating radar measurements were performed in August 2012, one along each ballast shoulder and one along the track centreline. The results of this study reveal that significant correlations between the observed track roughness and the ground-penetrating radar-based interpretation of ballast degradation are rare and only exist when the data are compared at very small spatial scales. The absence of significant correlations between track roughness and the estimates of ballast degradation is primarily interpreted as being the result of ambiguous ground-penetrating radar data caused by local-scale variations in the track foundation unrelated to ballast degradation. To address these issues, potential improvements in the application of ground-penetrating radar as a ballast degradation detection tool are proposed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".