Bibliographic record
Abstract
This paper applies a sequential trans-dimensional (trans-D) Monte Carlo algorithm for geoacoustic inversion to bottom-loss data estimated from wind-driven ambient noise at a drifting vertical array. The approach explored in this work provides range-dependent estimates of geoacoustic parameters and true-depth layering structure of the seabed, together with corresponding uncertainties. The Bayesian inversion is applied to incoherent estimates of seabed bottom loss, computed as the array drifts along a range-dependent track. The method adopts a layered representation of the seabed, where each layer is determined by sound speed, density, attenuation, and thickness. The number of layers is also included as an unknown parameter, which allows data-driven parametrization rather than an arbitrary choice of the parametrization for the seabed model. The trans-D Bayesian inversion method samples the joint posterior probability density of all model parameters to provide parameter estimates and uncertainties. A particle filter is used to update the estimated geoacoustic parameters from one array position to the next. The sequential inversion approach is demonstrated using data from the Boundary 2003 experiment, and compared to images of the seabed layering structure obtained by an active seismic system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".