Optimizing electromagnetic sensors for unexploded ordnance detection
Bibliographic record
Abstract
ABSTRACT Time-domain electromagnetic (TEM) instruments are the predominant geophysical sensor for detection of buried unexploded ordnance (UXO). Detection surveys commonly use towed TEM sensor arrays to acquire a digital map for target detection. We use a dipolar model to predict a detection threshold for a UXO at a specified clearance depth, given an arbitrary sensor geometry. In general, the minimum target response is obtained for a horizontally oriented target. We find that for multistatic sensors, the minimum response can also depend on the azimuth of the target. By considering the statistics of the target response, we find that the detection threshold can be raised slightly while still ensuring a high probability of detection of UXO at depth. This increase in the detection threshold can have a significant effect on the number of false alarms that need to be interrogated or investigated and hence on the cost of clearance. We also use Monte Carlo simulation to investigate how array geometry and height affect clutter rejection.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".