A Discrimination Algorithm for UXO Using Time Domain Electromagnetics
Bibliographic record
Abstract
An assumption is made that the Time Domain Electromagnetic (TEM) response of a buried axisymmetric metallic object can be modelled as the sum of two dipoles centered at the midpoint of the body. The strength of the dipoles depends upon the relative orientation between the object and the source field, and also upon the shape and physical properties of the body. Upon termination of the source field, each dipole is assumed to decay as k(t+α)−βe−t∕γ. The parameters k, α, β and γ depend upon the conductivity, permeability, size and shape of the object, and these can be extracted from the measurements by using a nonlinear parametric inversion algorithm. Investigations carried out using an analytic solution for a sphere and laboratory measurements of steel and aluminum rectangular prisms, suggest the following two-step methodology: (1) The value of β is first used as a diagnostic to assess whether the metallic object is non-magnetic or magnetic, (2) the ratios of k1∕k2 and β1∕β2 are then diagnostic indicators as to whether the geometry is plate-like (uninteresting) or rod-like (a high candidate for being a UXO). Results from the application of this algorithm to a TEM field data set acquired at the United States Army Corps of Engineers Research and Development Centre (ERDC) UXO Test Site have successfully identified a UXO to be magnetic and rod-like.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".