Transdimensional Inversion on the New England Mud Patch Using High-Order Modes
Bibliographic record
Abstract
This article presents geoacoustic inversion results for modal-dispersion data collected during the 2017 Seabed Characterization Experiment on the New England Mud Patch, an area where the seabed is characterized by an upper layer of mud. The experiment utilized a combustive sound source and a vertical line array of receivers at 5.4-km range. Using a careful combination of source deconvolution and warping time–frequency analysis, modal dispersion data (arrival time as a function of frequency) are estimated for 18 modes between modes 1 and 21. The modal dispersion data are then used to estimate seabed geoacoustic profiles and uncertainties via transdimensional Bayesian inversion. This article demonstrates the capacity to estimate high-order modes using warping. Comparing inversion results obtained with subsets of (lower order) modes to those obtained with the full set of available modes highlights the rich data information content carried by high-order modes. The results suggest a small sound-speed increase over the first 8 m of the seabed, the upper portion of the mud layer, which some earlier studies found to be isospeed. Overall, the inversion results are consistent within situmeasurements, as well as with previous geoacoustic inversion results.
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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 | 0.000 |
| 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".