Acoustic 3D computed tomography for demining and archeological applications
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 3D acoustic tomography concept of this paper combines computerized tomography image reconstruction algorithms using acoustic diffracting waves together with depth information to produce a novel, three-dimensional image of an underground section. The new method illuminates the ground with an array of acoustic sources along equally spaced points on the circumference of a surface of interest. 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 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 4 acoustic sources, providing plane waves, and a receiving line array of 32 acoustic sensors. Real results indicate that the performance characteristics of the new method are affected by the reflective acoustic energy from above the ground objects and the very poor signal-to-noise ratio of the received signal due to the poor air–ground coupling.
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 | 0.000 |
| 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) | 0.000 | 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