Bibliographic record
Abstract
This paper presents an analysis, based on existing work in geophysics and nondestructive testing, of the effects of soil electromagnetic properties on the functioning of metal detectors widely used to detect buried landmines. The host soil is modeled as a half-space having real and frequency-independent electrical conductivity but frequency-dependent complex magnetic susceptibility. The analysis technique has been applied to three examples of soil of practical importance, namely, nonconducting soil with frequency-independent susceptibility, nonconducting soil with frequency-dependent susceptibility, and nonmagnetic soil with constant conductivity. Simplifications are made to clearly explain a number of previous field and experimental observations, for example, the greater influence of magnetic properties than of electrical conductivity on the performance of metal detectors. Results also show that soil magnetic properties affect continuous wave and pulsed-induction detectors differently. The effect that electrical conductivity and magnetic susceptibility of the host soil have on the signal produced by a target is investigated by computing the response of a buried small metallic sphere. Computations show that in some cases, which could represent practical landmine detection scenarios, the signal from the soil can dominate that due to the target, making it hard to detect the target. Further, it is shown that magnetic soil can alter a target's spectral response, which implies that, contrary to present practice, object identification techniques should take into account the electromagnetic parameters of the host medium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".