Some reflections on Buckingham’s viscous grain shearing model
Bibliographic record
Abstract
Sediment geoacoustic properties are of considerable interest for commercial, industrial, and naval applications. In order to make geoacoustic inferences from acoustic data, the practitioner must choose a sediment acoustics model. The ‘Hamilton model’ is the most common, which assumes a frequency-independent sound speed, density, and an attenuation varying linearly with frequency, f1. Buckingham’s Viscous Grain Shearing (VGS) model offers a practical alternative with important benefits. For example, since it obeys causality, it provides fundamental bounds to the parameter space that the Hamilton model does not. Furthermore, if the acoustic data are sufficiently informative, the model provides insight into the frequency dependence of sound speed and attenuation. The central physics in VGS are expressed in high-level (material impulse response) functions, which consider both viscous and friction loss mechanisms and lead to attenuation varying between f1/2 to f1 to f2. Geoacoustic inference from seabed reflection measurements using the VGS model provides insight into the frequency dependence of naturally occurring marine sediments, i.e., admixtures of clay, silt, sand. At present, the VGS model is likely the most general (causal) sediment acoustics model, i.e., can reasonably treat the broadest range of sediment fabrics. [Research supported by the ONR Ocean Acoustics Program.]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".