Aeromagnetic surveying using a simulated unmanned aircraft system
Bibliographic record
Abstract
ABSTRACT Carleton University and Sander Geophysics are developing an unmanned aircraft system (UAS) for aeromagnetic surveying. As an early indication of the expected performance of the unmanned aircraft system, a simulated unmanned aircraft system (sUAS) was built. The simulated unmanned aircraft system is a T‐shaped structure configured as a horizontal gradiometer with two cesium magnetometers spaced 4.67 m apart, which is the same sensor geometry as planned for the unmanned aircraft system. The simulated unmanned aircraft system is flown suspended beneath a helicopter. An 8.5 km2 area in the Central Metasedimentary Belt of the Grenville Province, near Plevna, Ontario, Canada, was surveyed with the simulated unmanned aircraft system suspended 50 m above ground. The survey site was chosen on the basis of its complex geological structure. The total magnetic intensity (TMI) data recorded were compared to that obtained during a conventional fixed‐wing survey and a ground survey. Transverse magneto‐gradiometric data were also recorded by the simulated unmanned aircraft system. The simulated unmanned aircraft system total magnetic intensity data have a higher resolution than the conventional fixed‐wing data and were found to have a similar resolution to that of the ground survey data. The advantages of surveying with the simulated unmanned aircraft system were: (1) the acquisition of a detailed data set free of gaps in coverage at a low altitude above the terrain and (2) substantial saving of time and effort. In the survey site, the 4.67 m simulated unmanned aircraft system gradiometer measured the transverse magnetic gradient reliably up to an altitude of 150 m above ground.
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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 | 0.000 | 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) | 0.001 | 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 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".