Robotic magnetic mapping with the Kapvik planetary micro-rover
Bibliographic record
Abstract
Abstract Geomagnetic data gathering by micro-rovers is gaining momentum both for future planetary exploration missions and for terrestrial applications in extreme environments. This paper presents research into the integration of a planetary micro-rover with a potassium total-field magnetometer. The 40 kg Kapvik micro-rover is an ideal platform due to an aluminium construction and a rocker-bogie mobility system, which provides good manoeuvrability and terrainability. A light-weight GSMP 35U (uninhabited aerial vehicle) magnetometer, comprised of a 0.65 kg sensor and 0.63 kg electronics module, was mounted to the chassis via a custom 1.21 m composite boom. The boom dimensions were optimized to be an effective compromise between noise mitigation and mechanical practicality. An analysis using the fourth difference method was performed estimating the magnetic noise envelope at ±0.03 nT at 10 Hz sampling frequency from the integrated systems during robotic operations. A robotic magnetic survey captured the total magnetic intensity along three parallel 40 m long lines and a perpendicular 15 m long tie line over the course of 3.75 h. The total magnetic intensity data were corrected for diurnal variations, levelled by linear interpolation of tie-line intersection points, corrected for a regional gradient, and then interpolated using Delaunay triangulation to lead a residual magnetic intensity map. This map exhibited an anomalous linear feature corresponding to a magnetic dipole 650 nT in amplitude. This feature coincides with a storm sewer buried approximately 2 m in the subsurface. This work provides benchmark methodologies and data to guide future integration of magnetometers on board planetary micro-rovers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".