Gradient magnetometer dataset and MATLAB numerical code for simulating buried firearms at a controlled field site
Why this work is in the frame
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Bibliographic record
Abstract
Magnetic survey using multiple magnetometers to obtain gradiometric data can be used as a non-destructive method to search for buried firearms. We present magnetic dataset collected above a set of weapons buried at 0.6 m, 1.2 m, and 1.8 m depths. We provide three datafiles: two datafiles were collected on a coarse grid (1 m by 0.5 m) before and after burial of the weapons, and a third one collected on a fine grid (0.25 m by 0.1 m) after the burial of the weapons which concentrates on the area of buried firearms. We used a Gem Systems GSM-19GW Overhauser gradiometer consisting of two sensors with a relative vertical separation of 55 cm. Data acquisition was done via non-automated point measurements within a gridded measurement domain with data collection locations managed using measurement tape. Each field campaign resulted in about 3,000 data points. In addition, we developed a set of MATLAB scripts to model the magnetic anomalies (total field and gradient) for buried firearms, this set is also included here. The data and modeling scripts relate to a research article published in Forensic Science International (Deng et al., Suitability of magnetometry to detect clandestine buried firearms from a controlled field site and numerical modeling [1]). The dataset may be helpful for testing new algorithms for weapons detection while the numerical codes can be modified and applied for simulating magnetic anomalies resulting from similar buried objects with potential application in the sub-disciplines of forensic and archaeological geophysics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it