High-Precision Electrical Determination and Correction of Attitude Deviation for the Coil Vector Magnetometer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coil vector magnetometer is an advanced instrument that can perform integrated multi-element geomagnetic measurements and has excellent prospects for geoscience research and resource exploration applications. The attitude deviation is one of the main error sources of magnetic direction measurements of the coil vector magnetometer. The existing attitude determination method requires the use of additional auxiliary instruments (e.g., a spirit level). Furthermore, this method cannot be used to perform direct determination of the directional shift of the bias field caused by attitude deviation, and the magnetism of the detection instrument inevitably introduces new measurement errors. Therefore, it is difficult to achieve high-precision attitude deviation correction. To address this issue, we propose a novel electrical method to enable direct, high-precision determination of the attitude deviation and the corresponding correction indicator for the coil vector magnetometer by using only a single rotation of the magnetometer and applying bias fields, thereby realizing comprehensive high-precision hard and soft corrections of the attitude deviation via indicator alignment without relying on auxiliary detection instruments. In addition, we developed a dedicated experimental platform and then validated both the practicality and the performance of the proposed method in a geomagnetic observatory. Comparative experimental results for two coil vector magnetometers indicate that when the correction indicator’s alignment inaccuracy is less than 1 nT, the magnetic direction measurement error caused by the attitude deviation can be less than 6′′.
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.
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 it