<i>In situ</i> measurement technique for compressional wave velocity dispersion
Bibliographic record
Abstract
Marine sediments can be considered a two-phase medium composed of solid sediment particles and fluid-filled interstices. Many theories of propagation through such an assemblage predict a dispersive compressional wave velocity. Granular sediments (i.e., sands and gravels) have been predicted and reported to exhibit velocity dispersion and/or a nonlinear frequency-dependent attenuation in the frequency range 102−104 Hz (Kramers–Kronig relations require velocity dispersion if a nonlinear frequency-dependent attenuation is observed). One of the experimental challenges has been to measure velocity over several decades of frequency using a single technique so that observed velocity changes cannot be due to different biases in the measurement techniques. A simple in situ measurement technique is described that measures the critical angle as a function of frequency from ∼102−104 Hz and thus infers the velocity dispersion over that band. Measurements on the mid to outer continental shelf (Malta Plateau and the New Jersey shelf STRATAFORM area) show little or no velocity dispersion. This implies that the compressional wave attenuation for these sediments is linear or nearly linear over this band. [Work supported by the ONR and NATO Undersea Research Centre.]
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".