Detection and Location of Buried Infrastructures Using Ground Penetrating Radar
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
This article proposes an approach to improve the deployment of ground penetrating radar (GPR) in the field to detected and locate urban infrastructures. It consists of exploiting geographic data layers, database management systems, and a WebGIS, allowing users to handle GPR data within a georeferenced environment is developed based on a platform called GVX, providing users with four features, being (1) map integration, (2) geo-annotations and points of interest interaction, (3) radargram georeferencing, and (4) georeferenced slice visualization. Experiments with two categories of users, expert and non-expert GPR practitioners, have been performed. Based on the users' evaluation, the approach is valuable and can significantly improve GPR deployment. It helps users when discovering unmapped underground objects, delimiting the survey area, and interpreting GPR complex datasets. Overall, the approach optimized time and facilitated the spatial notion between GPR profiles and 3D meshes with map resources, allowing users to produce reliable maps, conforming to geospatial standards (CityGML).
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 | 0.001 |
| 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