Is Low Frequency Excess Noise of GMI Induced by Magnetization Fluctuations?
Bibliographic record
Abstract
We have investigated the possible impact of low frequency magnetization fluctuations on the equivalent magnetic excess noise of GMI sensors, which we have recently shown to exhibit 1/f noise. This noise component is not associated with the detailed measuring setup nor with the conditioning electronic noise sources, suggesting that it is intrinsic to the sensing element. Various intrinsic GMI noise sources might be able to explain this observation; these include magnetic domain wall motion, hysteresis loop losses, etc. Since GMI elements are excited by a high frequency current, it has been assumed that these low frequency (lf) intrinsic noise sources cannot interact with the carrier and the sensed signal. We recall that the GMI effect is based on an impedance variation, which is governed by magnetization angle variations. These are modulated by the sensed signal and the lf magnetization noise, which then appear as sidebands around the carrier frequency. Applying the fluctuation-dissipation theorem to the GMI model, we have related both the signal and noise to the magnetic susceptibility spectrum and thus quantified the equivalent magnetic noise of GMI sensors at lf. We then present a preliminary comparison to our previous experimental results.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".