Nondestructive imaging of shallow buried objects using acoustic computed tomography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The nondestructive three-dimensional acoustic tomography concept of the present investigation combines computerized tomography image reconstruction algorithms using acoustic diffracting waves together with depth information to produce a three-dimensional (3D) image of an underground section. The approach illuminates the underground area of interest with acoustic plane waves of frequencies 200-3000 Hz. For each transmitted pulse, the reflected-refracted signals are received by a line array of acoustic sensors located at a diametrically opposite point from the acoustic source line array. For a stratified underground medium and for a given depth, which is represented by a time delay in the received signal, a horizontal tomographic 2D image is reconstructed from the received projections. Integration of the depth dependent sequence of cross-sectional reconstructed images provides a complete three-dimensional overview of the inspected terrain. The method has been tested with an experimental system that consists of a line array of four-acoustic sources, providing plane waves, and a receiving line array of 32-acoustic sensors. The results indicate both the potential and the challenges facing the new methodology. Suggestions are made for improved performance, including an adaptive noise cancellation scheme and a numerical interpolation technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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