Detection of Buried Timber Trestles Using Surface Waves
Bibliographic record
Abstract
This paper presents results from multi-channel analysis surface waves tests (MASW) conducted on an earth embankment to detect the location of rotten buried trestles in two different sections (A and B). In Section A, the locations of the trestles are known as well as the soil properties; thus, this section is used for calibration purposes. In Section B, the trestle locations are unknown. A seismic array of 24 geophones with a geophone spacing of 0.5 m is used. Different signal processing techniques were used for the analysis of surface waves to compute dispersion curves, power spectral density functions, distance-frequency contour plot, and wavelet transforms. Numercial and experimental results show that MASW tests were able to detect the location of buried trestles. MASW tests with a low-energy source (sledgehammer test) clearly show the location of buried trestles. Timber trestles can be detected by plotting the mean square value of the vibration energy. The effects of the trestles are also observed in the dispersion curves, the distance-frequency contour plot, and the Morlet wavelet transform. Not all source locations showed the location of timber trestles, likely because of the stronger effect of the ballast layer in this test.
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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 | 0.000 |
| 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".