Geoacoustic inversion of modal dispersion on the New England Mud Patch. II: Hybrid parameterization for gradient estimation
Bibliographic record
Abstract
This paper applies Bayesian geoacoustic inversion with a hybrid seabed model parameterization to modal-dispersion data from the New England Mud Patch to attempt to estimate gradient structure in the upper mud layer. The hybrid seabed parameterization is based on an upper layer with a general representation of gradients based on Bernstein-polynomial basis functions, above an unknown number of discrete (uniform) layers formulated trans-dimensionally. The dataset, collected during the 2017 Seabed Characterization Experiment (SBCEX17), involves high-order modes (up to mode 21) extracted via warping time-frequency analysis from recordings of a combustive sound source on a vertical hydrophone array. The inversion results are compared to those from a trans-dimensional inversion of the same data with no gradient layer, and to results from other acoustic data sets collected in the region as well as to nearby core measurements.
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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 | 0.000 | 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".