A Procedure for Quantifying a UAV-borne Magnetometer Vertical Setback Distance
Bibliographic record
Abstract
Summary Within this study, a vertical buzz test methodology is applied to characterize the distance at which the electromagnetic interference generated by a UAV platform attenuates below the sensitivity threshold of a high-resolution magnetometer in a controlled setting. A DJI Wind 4 heavy-lift, multi-rotor UAV platform was used to characterize the spatial extent of the electromagnetic interference generated inflight. The vertical setback distance of a UAV-borne aeromagnetic system was characterized using a vertical buzz test maneuver in a magnetically quiet area. Through conducting the characterization test, it was determined that the DJI Wind 4 with a 2.2 kg payload required a vertical setback distance of approximately 5 m when surveying with a magnetometer employing a sensitivity of 0.01 nT. Furthermore, it was determined that a magnetometers vertical setback distance is unique for each specific combination of UAV platform and magnetometer employed within a UAV-borne aeromagnetic system. Based on previous tests, using the same magnetometer and methodology, the vertical setback distance was determined to be 3 m, for both a DJI - S900 and M600. Therefore, the assessment shown herein should be conducted to characterize the vertical setback distance for specific UAV magnetometry systems (each platform and magnetometer) prior to conducting surveys.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".