Standardized Down-Looking Ground-Penetrating Radar (DLGPR) data collections
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".