Characterization of Noise Produced During Continuous and Sparse Sampling Functional Magnetic Resonance Imaging
Bibliographic record
Abstract
Noise is generated during magnetic resonance imaging (MRI) and comes from the gradient magnetic field (Lorentz forces acting on the gradient coils) and radiofrequency pulses used to generate sequences for scanning. Characterizing this noise is complicated by the fact that sensors without any metal parts must be used, given the intense magnetic fields present in MRI. Also, the typical continuous acquisition scheme that generates continuous noise is hardly compatible with auditory-related experiments. To avoid noise during stimulus presentation, sparse samplingfunctional MRI has been suggested and involve theacquisition of imaging volumes interspersed with silent periods (i.e. no acquisition periods).This communication describes the use of optical fiber microphones to characterize the noise produced during continuous and sparse sampling functional magnetic resonance imaging sequences. The whole instrumentation chain is described, along with the results obtained in terms of overall sound pressure level and time-frequency content.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".