Bibliographic record
Abstract
Aeromagnetic data is one of the most widely collected types of data in exploration geophysics. With the continuing prevalence of unmanned air vehicles (UAVs) in everyday life there is a strong push for aeromagnetic data collection using UAVs. However, apart from the many political and legal barriers to overcome in the development of UAVs as aeromagnetic data collection platforms, there are also significant scientific hurdles, primary of which is magnetic compensation. This is a well-established process in manned aircraft achieved through a combination of platform magnetic de-noising and compensation routines. However, not all of this protocol can be directly applied to UAVs due to fundamental differences in the platforms, most notably the decrease in scale causing magnetometers to be significantly closer to the avionics. As such, the methodology must be suitably adjusted. The National Research Council of Canada has collaborated with Aeromagnetic Solutions Incorporated to develop a standardized approach to de-noising and compensating UAVs, which is accomplished through a series of static and dynamic experiments. On the ground, small static tests are conducted on individual components to determine their magnetization. If they are highly magnetic, they are removed, demagnetized, or characterized such that they can be accounted for in the compensation. Dynamic tests can include measuring specific components as they are powered on and off to assess their potential effect on airborne data. The UAV is then flown, and a modified compensation routine is applied. These modifications include utilizing onboard autopilot current sensors as additional terms in the compensation algorithm. This process has been applied with success to fixed-wing and rotary-wing platforms, with both a standard manned-aircraft magnetometer, as well as a new atomic magnetometer, much smaller in scale.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".