SuMag: Suspended Magnetometer Survey for Mineral Data Acquisition with Vertical Take-off and Landing Fixed-wing Aircraft
Bibliographic record
Abstract
Multirotor Uncrewed Aerial Vehicle (UAV)s have recently become an important instrument for the magnetic method for mineral exploration (MMME), enabling more effective and accurate geological investigations. This paper explores the difficulties in mounting high-sensitivity sensors on a UAV platform, including electromagnetic interference, payload dynamics, and maintaining stable sensor performance while in flight. It is highlighted how the specific solutions provided to deal with these problems have the potential to alter the collection of data using the MMME, assisted by UAVs. The work also shows experimental findings that demonstrate the creative potential of these solutions in UAV-based data collection for the MMME, leading to improvements in effective mineral exploration through careful design, testing, and assessment of these systems. These innovations resulted in a platform that is quickly deployable in remote areas and able to operate more efficiently compared to traditional crewed aircraft or multirotor UAVs while still producing equal or higher quality results. This allows for much higher efficiency and lower operating costs for high production UAV-based data collection for the MMME.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".