Characterizing mine detector performance over difficult soils
Bibliographic record
Abstract
A variety of metal detectors are available for the detection of buried metallic targets in general and for humanitarian demining in particular. No one detector is optimal in all environments: variations in soil conductivity, and more importantly, frequency dependent soil magnetic susceptibility can favor one design over another. The use of computer modeling for assessing different designs is straightforward in principle, at least to first order, but still difficult in practice. The Geophysics Lab of the University of Toronto is attempting to address this problem in two ways. The first is by assembling the required computational algorithms to do this into a single simulation code with a straightforward GUI, intended to be public domain as a MATLAB code. The second, the subject of a companion paper in this conference, is by making measurements of the electromagnetic properties of difficult soils, and finding semi-analytic representations of these responses suitable for modeling purposes. The final version of the code, when completed, is to handle single or multiple transmitter and receiver coils of circular or polygonal shape, general transmitter current waveforms, arbitrary transmitter orientations and survey paths, small targets with frequency-dependent anisotropic responses (permitting both magnetic and inductive responses to be calculated), embedded in multi-layered half spaces with both conductivity and frequency-dependent susceptibility (so-called "difficult soils"). The current state of the simulation code and examples of its use will be described in this paper.
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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".