Instrumentation and application of unmanned ground vehicles for magnetic surveying
Bibliographic record
Abstract
With the recent proliferation of unmanned aerial vehicles (UAV) for geophysical surveying a novel opportunity exists to develop unmanned ground vehicles (UGV) in parallel. This research presents a pilot study to integrate two UGVs, the Kapvik planetary micro-rover and a Husky A200 robotic development platform, with a GSMP 35U magnetometer that has recently been developed for the UAV market. Magnetic noise levels generated by the UGVs in laboratory and field conditions are estimated using the fourth difference method and, at a magnetometer-UGV separation distance of 121 cm, the Kapvik micro-rover was found to generate a noise envelope 0.04 nT whereas the noisier Husky UGV generated an envelope of 3.94 nT. The UGVs were assessed over a series of successful robotic mapping missions which demonstrated their capability for magnetic mapping, and their productivity and versatility in field conditions.
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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".