Compressed sensing based tuning algorithm for the sensor of proton precession magnetometers
Bibliographic record
Abstract
In this Note, a new compressed sensing-based tuning algorithm has been developed to boost the sensor tuning performance of the proton precession magnetometers (PPMs). An end-to-end framework for the PPM's sensing free induction decay (FID) signal resonance based on orthogonal matching pursuit compressed sensing (OMPCS), dubbed OMPCS-FID resonance (OMPCS-FIDR), is developed and its working principle and implemented strategy are elaborated. By comparing the new sensor tuning approach with the state-of-the-art algorithms, i.e., peak detection, auto-correction, and secondary tuning, the results demonstrate that the proposed tuning method not only retains the performance but also overcomes the drawbacks of the state-of-the-art methods, which accelerates the possibilities of the PPM working in a scenario with a strong gradient magnetic field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".