Shear-wave-velocity profiling of a test embankment for a high-speed rail project using the spectral-analysis-of-surface-waves (SASW) method
Bibliographic record
Abstract
Nondestructive seismic testing was conducted at 24 locations on and around the embankment to evaluate the compaction effort and lateral variabilities at the site. This embankment was being constructed as a test embankment for a high-speed railroad section in the USA. Different compaction techniques were used in different embankment areas, and nuclear-gauge density measurements were performed to evaluate the compaction quality in all areas. Additionally, Spectral-Analysis-of-Surface-Waves (SASW) testing was performed to determine the Vs profiles at numerous locations on and around the embankment. The Vs values determined from the SASW testing were used to compare and evaluate areas where different compaction methods were used, as well as to help understand the overall site. The embankment was divided into three zones. In each zone, five shorter and one longer SASW arrays on top of the embankment were used to determine the Vs profiles. Also, in each zone, Vs profiles in the natural soil were determined using two additional long SASW arrays on the natural ground, one array on each longitudinal side of the embankment. These 24 Vs profiles on and around the embankment were used to evaluate lateral variability in the shear stiffnesses of the geotechnical materials at the site.
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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 | 0.000 |
| 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".