Bibliographic record
Abstract
Diffusing Acoustic Wave Spectroscopy (DAWS) is an ultrasonic technique that has been developed to measure the dynamics of heterogeneous media from the temporal fluctuations of multiply scattered waves. This technique is similar to more recent developments in field fluctuation spectroscopy, called coda wave interferometry, that use variations in the seismic coda to infer changes in the medium with time. After reviewing the basic principles on which Diffusing Acoustic Wave Spectroscopy is based [M. L. Cowan, J. H. Page, and D. A. Weitz, Phys. Rev. Lett. 85, 453 (2000); Phys. Rev. E 65, 066605 (2002)], its potential as a sensitive method for probing the dynamics of strongly scattering materials will be illustrated with recent experiments on fluidized suspensions of particles. In this type of system, DAWS measures the local relative motion of the scatterers (or strain rate) on a length scale determined by the transport mean free path of the multiply scattered waves. When combined with the complementary technique of Dynamic Sound Scattering using singly scattered waves, DAWS can also determine the instantaneous velocity correlation length of the moving scatterers, thus giving a quite complete picture of the system dynamics over a wide range of length and time scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".