High-Precision Electrical Determination and Correction of Attitude Deviation for the Coil Vector Magnetometer
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′′.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".