Diffusing acoustic wave spectroscopy
Bibliographic record
Abstract
We have developed a technique in ultrasonic correlation spectroscopy called diffusing acoustic wave spectroscopy (DAWS). In this technique, the motion of the scatterers (e.g., particles or inclusions) is determined from the temporal fluctuations of multiply scattered sound. In DAWS, the propagation of multiply scattered sound is described using the diffusion approximation, which allows the autocorrelation function of the temporal field fluctuations to be related to the dynamics of the multiply scattering medium. The expressions relating the temporal field autocorrelation function to the motion of the scatterers are derived, focusing on the types of correlated motions that are most likely to be encountered in acoustic measurements. The power of this technique is illustrated with ultrasonic data on fluidized suspensions of particles, where DAWS provides a sensitive measure of the local relative velocity and strain rate of the suspended particles over a wide range of time and length scales. In addition, when combined with the measurements of the rms velocity of the particles using dynamic sound scattering, we show that DAWS can be used to determine the spatial extent of the correlations in the particle velocities, thus indirectly measuring the particle velocity correlation function. Potential applications of diffusing acoustic wave spectroscopy are quite far reaching, ranging from the ultrasonic nondestructive evaluation of the dynamics of inhomogeneous materials to geophysical studies of mesoscopic phenomena in seismology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".