Impact of Electronic Conditioning on the Noise Performance of a Two-Port Network Giant MagnetoImpedance Magnetometer
Bibliographic record
Abstract
The performance of giant magneto-impedance (GMI)-based magnetometers is currently limited by the noise due to the electronic conditioning circuitry. We propose a simple model of this noise for a GMI sensor using a synchronous detection scheme. The GMI sensing element consists of a thin pick-up coil wound around a Co-rich amorphous micro-wire. It is fully described by a two port network model and associated impedance matrix. Noise and sensitivity behavior are studied for the four measuring configurations, corresponding to four terms of the impedance matrix. The model yields a good description of experimental data from noise measurements. The magnetic noise spectral density is dominated either by the excitation or detection stages, depending upon whether the excitation currents are high or low. The nontrivial noise behavior exhibited by each configuration leads to better understanding of the noise limitations of GMI magnetometers. The configuration in which the signal at the coil terminals is measured (often called offdiagonal) is the most efficient in decreasing the equivalent output magnetic noise spectral density.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".