Interpretation of High-Resolution Low-Altitude Helicopter Magnetometer Surveys Over Sites Contaminated with Unexploded Ordnance
Why this work is in the frame
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Bibliographic record
Abstract
SummaryThrough-out the world, millions of acres of potentially productive land are contaminated with unexploded ordnance due to either past-conflicts or to military training. Low-level helicopter magnetometry (HeliMag) is currently being used to rapidly cover large areas and identify regions that are potentially clear of hazardous munitions. The configuration we currently use comprises seven cesium vapour magnetometers, horizontally spaced 1.5 meters apart on a boom several meters in front-of a Bell 206L helicopter. Magnetometer data are collected at 400 Hz at altitudes as low as 1.5 m above the ground along transects spaced 7 meters apart. From this dense, high-resolution data, potential metallic targets as small as an 81 mm mortar are identified using manual and/or automatic picking methods. The target picks are then used to estimate densities of potential contamination. 100% detection is generally not feasible, so that HeliMag is usually applied in a characterization rather than in a clearance mode. We describe a HeliMag survey collected over a UXO contaminated site at Yekau Lake, near Edmonton, Canada. The objective was to identify the location and extent of an 11.5 pound bomb target area. The target density estimates derived from manual picks were strongly influenced by geology and clutter and did not reflect the underlying density of ordnance and ordnance related clutter. By fitting a dipole model to each target pick, and comparing it to the expected response of the target item, we could estimate the density of objects with similar size/shape to an 11.5 pound bomb. This analysis clearly identified an area of elevated contamination in the same region where 11.5 pound bombs were found during ground reconnaissance. In summary, the new methodology significantly improves the interpretability of HeliMag data when used for UXO site assessment.
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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 | 0.002 | 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) | 0.001 | 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 it