Gradient magnetometer dataset and MATLAB numerical code for simulating buried firearms at a controlled field site
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".