Standardized Down-Looking Ground-Penetrating Radar (DLGPR) data collections
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
Down-looking ground penetrating radar (DLGPR) has been used extensively for buried target detection. Performance of a DLGPR is typically measured by calculating the probability of detection (PD) and the false alarm rate (FAR) against a target set in a particular soil type. Variability in target sets, including target construction, size, layout, and burial depth, make comparing performance of a DLGPR across test sites and soil compositions a challenge. This paper describes a recent effort to collect data against a standardized set of target types, layouts, and depths. The goal of this effort is to have data sets collected in a uniform manner at various test sites in Australia and Canada for more meaningful comparisons of DLGPR performance in a range of soil types. The data is to be used to improve algorithms for the automatic detection of targets. This paper will describe test planning and execution, and discuss high-level DLGPR results and ongoing analyses from the Australian data collection.
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 | 0.000 |
| 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