The effect of winding and core support material on the thermal gain dependence of a fluxgate magnetometer sensor
Bibliographic record
Abstract
Abstract. Fluxgate magnetometers are an important tool in geophysics and space physics but are typically sensitive to variations in sensor temperature. Changes in instrumental gain with temperature, thermal gain dependence, are thought to be predominantly due to changes in the geometry of the wire coils that sense the magnetic field and/or provide magnetic feedback. Scientific fluxgate magnetometers typically employ some form of temperature compensation and support and constrain wire sense coils with bobbins constructed from materials such as MACOR machinable ceramic (Corning Inc.) which are selected for their ultra-low thermal deformation rather than for robustness, cost, or ease of manufacturing. We present laboratory results comparing the performance of six geometrically and electrically matched fluxgate sensors in which the material used to support the windings and for the base of the sensor is varied. We use a novel, low-cost thermal calibration procedure based on a controlled sinusoidal magnetic source and quantitative spectral analysis to measure the thermal gain dependence of fluxgate magnetometer sensors at the ppm°C−1 level in a typical magnetically noisy university laboratory environment. We compare the thermal gain dependence of sensors built from MACOR, polyetheretherketone (PEEK) engineering plastic (virgin, 30 % glass filled and 30 % carbon filled), and acetal to examine the trade between the thermal properties of the material, the impact on the thermal gain dependence of the fluxgate, and the cost and ease of manufacture. We find that thermal gain dependence of the sensor varies as one half of the material properties of the bobbin supporting the wire sense coils rather than being directly related as has been historically thought. An experimental sensor constructed from 30 % glass-filled PEEK (21.6 ppm°C−1) had a thermal gain dependence within 5 ppm°C−1 of a traditional sensor constructed from MACOR ceramic (8.1 ppm°C−1). If a modest increase in thermal dependence can be tolerated or compensated, then 30 % glass-filled PEEK is a good candidate for future fluxgate sensors as it is more economical, easier to machine, lighter, and more robust than MACOR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".