Electrical impedance tomography for underwater detection of buried mines
Bibliographic record
Abstract
The detection of buried land mines in soil is a well-studied problem; many existing technologies are designed and optimized for performance in different soil types. Research on mine detection in shallow water environments such as beaches, however, is much less developed. Electrical impedance tomography (EIT) shows promise for this application. EIT uses current-stimulating and voltage-recording electrode pairs to measure trans-impedances in the volume directly beneath the electrode array, which sits flat over the ground surface. The trans-impedances are used to construct a conductivity profile of the volume. Non-metallic and metallic explosives appear as perturbations in the conductivity profile, and their location and size can be estimated. Lab testing has yielded promising results using a submerged array positioned over a sand bed. The instrument has also successfully detected surrogate mines in a traditional soil environment during field trials. Resolution of the detector is roughly half the pitch of electrodes in the array. In underwater lab testing, non-conducting targets buried in the sand are detected at a depth of 1.5 times the electrode pitch with the array positioned up to one electrode pitch above the sand bed. Results will be presented for metallic and non-metallic targets of various shapes and sizes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".