Construction of an Overhauser magnetic gradiometer and the applications in geomagnetic observation and ferromagnetic target localization
Bibliographic record
Abstract
The proton precession magnetometer with single sensor is commonly used in geomagnetic observation and magnetic anomaly detection. Due to technological limitations, the measurement accuracy is restricted by several factors such as the sensor performance, frequency measurement precision, instability of polarization module, etc. Aimed to improve the anti-interference ability, an Overhauser magnetic gradiometer with dual sensor structure was designed. An alternative design of a geomagnetic sensor with differential dual-coil structure was presented. A multi-channel frequency measurement algorithm was proposed to increase the measurement accuracy. A silicon oscillator was adopted to resolve the instability of polarization system. This paper briefly discusses the design and development of the gradiometer and compares the data recorded by this instrument with a commonly used commercially Overhauser magnetometer in the world market. The proposed gradiometer records the earth magnetic field in 24 hours with measurement accuracy of ± 0.3 nT and a sampling rate of 3 seconds per sample. The quality of data recorded is excellent and consistent with the commercial instrument. In addition, experiments of ferromagnetic target localization were conducted. This gradiometer shows a strong ability in magnetic anomaly detection and localization. To sum up, it has the advantages of convenient operation, high precision, strong anti-interference, etc., which proves the effectiveness of the dual sensor structure Overhauser magnetic gradiometer.
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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 | 0.001 |
| 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) | 0.001 | 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".