Interpretation of High-Resolution Low-Altitude Helicopter Magnetometer Surveys Over Sites Contaminated with Unexploded Ordnance
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".