Measurement of rail deflection on soft subgrades using DIC
Why this work is in the frame
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Bibliographic record
Abstract
The measurement of track displacement during the passage of a train is an important parameter for the assessment of track condition. Digital image correlation (DIC) is a non-contact camera-based technology that can be used to measure these displacements. However, ground vibrations induced by the train can result in camera movement, adding error to the measured displacement. This paper presents a two-camera method that can account for the camera movement when measuring track displacements using DIC. The method is validated on a stationary track and then used to measure track displacement during the passage of two trains travelling at different velocities. The results of the two-camera method are then compared to the track displacements found using a low-pass filter. The two-camera method was found successfully to reduce error due to camera movement while removing the subjectivity of choosing a cut-off frequency for filtering.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it