Historical Development and Performance of Airborne Magnetic and Electromagnetic Systems for Mapping and Detection of Unexploded Ordnance
Bibliographic record
Abstract
Abstract Over the past fifteen years, notable progress has been made in the performance of airborne geophysical systems for mapping and detection of unexploded ordnance in terrestrial and shallow marine environments. For magnetometer systems, the most significant improvements include development of boom-mounted platforms, and implementation of higher sample rates, denser magnetometer arrays, and vertical gradient configurations. Nine magnetometer-based systems are described and their performance summarized. In prototype analyses and recent U.S. Department of Defense Environmental Security Technology Certification Program (ESTCP) assessments using new production systems, the best performance has been achieved with a vertical gradient configuration. As effective as magnetometer systems have proven to be at many sites, they are inadequate at sites where basalts and other ferrous geologic formations or soils produce anomalies that approach or exceed those of target ordnance items. Additionally, magnetometer systems are ineffective where detection of non-ferrous ordnance items is of primary concern. We discuss the development of airborne time-domain electromagnetic systems over the past ten years. Overall, improvements in airborne geophysical systems have led to more consistent detection of smaller ordnance. These trends should continue as additional technological advances are made.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".