Representation of depth-dependent gradients in sediment geoacoustics by Bernstein polynomials
Bibliographic record
Abstract
We present a seabed parametrization approach for depth-dependent gradients in sediment geoacoustics, a property commonly observed in muds. The method represents continuous functions by a polynomial form, consisting of a finite sum of Bernstein basis weighted by real coefficients which are estimated by Bayesian geoacoustic inversion of seabed reflectivity data. The advantages of the Bernstein representation of continuous gradients are discussed, including efficiency in representing a wide variety of gradients with only a few coefficients, as well as high numerical stability of the polynomial form to perturbation of its coefficients. The performance of the Bernstein parametrization applied to geoacoustic inversion is illustrated with simulated data obtained from a realistic seabed scenario. In addition, the Bernstein approach is applied to experimental data from four mud sites at the Malta Plateau. The estimated geoacoustic profiles are in good agreement to core measurements from the area, and serve to illustrate the ability of the Bernstein-based inversion to represent steep gradients. Comparison to results obtained by discrete (multi-layered) and other continuous gradient representations (line-, sinusoid-, and spline-based) is presented.
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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 | 0.000 |
| 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".