Comparison of Performance of Airborne Magnetic and Transient Electromagnetic Systems for Ordnance Detection and Mapping
Bibliographic record
Abstract
Abstract Magnetic and electromagnetic data collected by helicopter boom-mounted systems at three different sites permit direct comparison of the systems as to their suitability for buried ordnance detection, mapping, and discrimination. Airborne boom-mounted magnetic systems are at a more advanced stage of development than their electromagnetic counterpart. However, in basaltic terrain, transient electromagnetic systems have proved capable of detecting buried ordnance, whereas magnetic systems may fail to detect ordnance altogether. Magnetic systems use passive sensors and these can be distributed along the boom structure such that dense data can be collected with sensors spaced 1–2 m apart over a broad swath, up to 12-m wide. The ORAGS-TEM electromagnetic system, having only two receivers, must rely on interleaved flight lines to obtain data of a spatial density approaching that of the airborne magnetic systems. The total magnetic fields from unexploded ordnance decays at 1/R3 for total field and its gradient at 1/R4. This permits adequate signal-to-noise levels to be easily attained for larger ordnance types at survey heights up to seven meters. Active electromagnetic fields decay at between1/R4 and 1/R6, depending on ordnance type and sensor geometry, and this constrains current electromagnetic systems to practical survey altitudes of less than three meters. Tests at the Badlands Bombing Range indicate that, in some circumstances, the signal-to-noise for the airborne electromagnetic system exceeds that of airborne magnetic systems, and even ground electromagnetic systems. Because time must be allowed for transmitter current buildup and decay, ORAGS-TEM is not capable of sampling along line at the same spatial density as can magnetic systems. However, the temporal signal decay permits greater opportunity for ordnance discrimination than magnetic measurements.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".