Towards drone-based magnetometer measurements for archaeological prospection in challenging terrain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While airborne magnetometry has been used for geological surveys for decades, magnetic surveys for archaeological prospection are almost exclusively ground-based, as the detection of archaeological features requires higher spatial resolution and close proximity between sensor and object. However, the recent development of drones and magnetic sensors allows for low-altitude drone-based surveys, which are an interesting alternative for magnetic prospection of challenging areas, where vegetation, difficult terrain, access restrictions, or safety concerns hamper ground-based surveys. In this paper, we present test measurements in challenging areas in Germany and Switzerland, which demonstrate the potential as well as technical and practical concerns of drone-based magnetometry for archaeological prospection. We used a miniature total-field magnetometer, which was tethered to an octocopter drone. Although it is preferable to fly the sensor close to the ground where anomalies show the highest values, we could also detect magnetic anomalies in altitudes up to few metres above ground. Flights at different altitudes show the decay and widening of the anomalies with height. Drone-based magnetic measurements in rough and vegetated terrain require careful flight planning based on a high-resolution surface model. Further development is needed to improve positioning accuracy of the tethered magnetometer and to improve heading error correction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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