Identification Of Uxo With Surface Magnetic Charges On A Sphere
Bibliographic record
Abstract
Discrimination of buried unexploded ordnance (UXO) with electro-magnetic sensors requires robust predictive models to correctly interpret data recorded at the surface. Shubitidze et al. have recently suggested a representation of the Electromagnetic Induction (EMI) response of a metallic object by using a model of Normalized Surface Magnetic Charges (NSMC) distributed on a spheroidal surface enclosing the target. Their results suggest that the Total Normalized Magnetic Charge (TNMC), the integral of NSMC over the spheroid, can be used to identify the object. The interpretation however is much simpli ed by using a sphere. Theoretical considerations show that the scattered eld of a spherical object is purely dipolar. The associated NSMC istribution is uniform on a sphere and the TNMC is directly related to the magnetization tensor and sphere radius. These concepts have impacted upon our approach where we estimate the charge distribution on the surface of the sphere by solving a linear inverse problem. An additional bene t of using a sphere instead of a spheroid is that it is no longer necessary to specify the orientation parameters of the buried object. Azimuth and dip are instead revealed in the recovered surface charge distribution. We nd that this formulation helps develop a robust NSMC that has potential for practical discrimination of UXO. We demonstrate our approach by using Geonics EM63 data collected at the USACE-ERDC test stand in Vicksburg, MS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".