Detection of Optical–Magnetic Axis Inconsistency in Multifunctional Magnetometers
Bibliographic record
Abstract
Multifunctional magnetometers enable high-precision observations of the geomagnetic direction and total magnetic field, which have broad applications in geoscience research and space weather forecasting. However, inconsistencies still occur between the optical and magnetic axes of classic multifunctional magnetometers, and few studies have investigated their direct detection. To address this issue, we propose a novel method for the detection of the optical-magnetic axis inconsistency. The magnetic azimuths for the magnetic axis and the optical axis of the multifunctional magnetometer are determined through the bias field loading and fluxgate theodolite, thereby allowing direct detection of the inconsistency. The proposed method is also modeled to quantify the detection errors introduced by the total-field sensor and the observation operation. In addition, we develop a dedicated experimental platform and conduct comparative tests at a geomagnetic observatory to validate the practicality and performance of the method. The experimental results show that the proposed method can significantly reduce the deviation of the declination curve measured by the multifunctional magnetometer relative to the curve measured by the fluxgate theodolite from$3959{}^{\prime \prime }$to$19{}^{\prime \prime }$, achieving a 99.5% reduction rate.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".