Gradient representations in seabed geoacoustic inversion by Bernstein polynomials
Bibliographic record
Abstract
Geoacoustic properties of the upper-most transition layer of mud seabed sediments often change rapidly with depth as continuous gradients, rather than discontinuous layers. However, most geoacoustic inversion approaches are based on layered sediment models. This paper presents a seabed parameterization approach that represents continuous geoacoustic gradients as a sum of Bernstein polynomial basis functions weighted by unknown coefficients which are estimated by Bayesian inversion of seabed acoustic reflectivity data. The Bernstein representation is efficient/effective in representing a wide variety of gradients with a small number of coefficients, and has optimal numerical stability to perturbation of the coefficients in the nonlinear inversion scheme. The Bernstein parametrization in geoacoustic inversion is illustrated with simulated data and with experimental data from four mud sites on the Malta Plateau in the Strait of Sicily. The inversion results are in good agreement with sound speed and density estimates from co-located sediment cores, and serve to illustrate the ability of the Bernstein polynomial parameterization to represent steep and strongly-variable gradients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".