Evaluating the Effectiveness of Varying Transmitter Waveforms for UXO Detection in Magnetic Soil Environments
Bibliographic record
Abstract
Magnetic soils are a major source of false positives when searching for landmines or unexploded ordnance (UXO) with electromagnetic induction sensors. In adverse areas up to 30% of identified electromagnetic (EM) anomalies are attributed to geology. The EM phenomena that give rise to the response of magnetically viscous soil and metal are fundamentally different. The viscosity effects of magnetic soil can be accurately modelled by assuming a ferrite relaxation with a log-uniform distribution of time constants. The EM response of a metallic target is due to eddy currents induced in the target and is a function of the target's size, shape, conductivity and magnetic susceptibility. In this presentation, we compare the range and variation of time decays of metallic targets (UXO and scrap metal) with the time domain electromagnetic response of magnetically viscous ground. Electromagnetic sensor measurements and modelling are used to assess the feasibility of multiple pulse widths to differentiate the ground response from metallic targets. We present ground and metal responses measured by Geonics EM61-MK2 sensors with differing transmitter waveform characteristics.
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 | 0.001 | 0.010 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".