Uncertainty quantification and spatial variability of velocity- and attenuation-frequency dependence along a 14-km seabed survey on the Malta Plateau
Bibliographic record
Abstract
We study compressional-wave frequency dependence of sound velocity and attenuation in the seabed by inverting reflectivity data recorded by an autonomous underwater vehicle (AUV) on the Malta Plateau along a 14-km survey track. The AUV towed a 32-hydrophone array and a source emitting signals at ~4-m intervals in two frequency bands (900–1300 and 1900–3600 Hz). The reflection data are processed in terms of reflection coefficients which results in ~1500 data sets, each with a seabed footprint of <20 m. For efficient Bayesian uncertainty quantification, a trans-dimensional particle filter is applied. The dataset provides a usable frequency bandwidth of 1000–3400 Hz to study velocity- and attenuation-frequency dependence which is modelled with viscous grain shearing theory. The trans-dimensional model allows frequency-dependence inferences as a function of depth while fully accounting for the unknown seabed stratification which substantially affects the estimates. Finally, the AUV acquisition provides the means to study the frequency-dependent seabed variability at mesoscales of several meters which are poorly understood. [Data are from CLUTTER JRP, a collaboration of ARL-PSU, DRDC, CMRE, and NRL. Research supported by the Natural Sciences and Engineering Research Council of Canada.]
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".