Uncertainty estimation of sound attenuation in marine sediments at low frequencies.
Bibliographic record
Abstract
Marine sediment attenuation at low frequencies (under 5 kHz) is generally difficult to be directly measured by in situ probes embedded in the sediment, partly due to the very short propagation distances. An alternate experimental technique is to use single bottom bounce signals received by a vertical line array. The frequency dependence of the sediment attenuation is first obtained by comparing the amplitude differences of the sea floor reflection and the sub bottom layer reflection at different frequencies. The absolute attenuation is then obtained by using the previously estimated sound speed and layer thickness. Inherently there is uncertainty introduced in each stage of the attenuation estimation procedure. To evaluate the uncertainty of the attenuation estimate, the standard deviation of the signal fluctuation is mapped to the intermediate result first, and then Bayesian inversion results of the sound speed and the layer thickness are included in the final attenuation estimates. This uncertainty analysis is demonstrated by the estimation of the sediment attenuation from the low frequency chirp data collected in a variable water column environment in the Shallow Water 06 experiment. [Work supported by ONR Ocean Acoustics.]
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".