A preliminary investigation of the effects of soil electromagnetic properties on metal detectors
Bibliographic record
Abstract
Some soils can adversely affect the operation of sensitive metal detectors widely used to detect buried landmines. Although there has been some related work in geophysics, researchers in metal detection techniques, until very recently, seem to have largely ignored the issue of problem soil. As a result, rigorous scientific investigations of how soil electromagnetic properties may affect the operation of metal detectors are lacking. Thus, there is a need for theoretical and experimental studies to clarify which electromagnetic properties are important and to what extent they affect the performance of metal detectors of various designs. The paper presents a systematic analytical framework, based on existing work in geophysics and non-destructive testing, for studying the effects of soil electromagnetic properties on the functioning of metal detectors. For this initial study the burial medium is modelled as a half-space. While soil electrical conductivity has been assumed to be real and independent of frequency, soil magnetic susceptibility has been modelled as complex and frequency dependent. Simplified versions of the analysis techniques have been applied to three selected cases of practical importance, namely, non-conducting soil with constant susceptibility, non-conducting soil with frequency-dependent susceptibility and non-magnetic soil with constant conductivity. Results from a preliminary analysis of even these simple cases explain a number of previous experimental observations, for example, the greater influence of magnetic properties than of electrical conductivity on the performance of metal detectors.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".