A general model for sediment-column structure on the New England Mud Patch from Bayesian geoacoustic inversion of seabed reflection data
Bibliographic record
Abstract
Muddy sediments cover significant portions of continental shelves, but their physical properties remain poorly understood compared to sandy sediments. To explore the spatial and frequency dependencies of mud properties, wide-angle seabed reflection coefficients versus grazing angle and frequency were measured on the New England Mud Patch (NEMP) during the 2017 Seabed Characterization Experiment. This paper presents trans-dimensional Bayesian inversion of reflection coefficients within a frequency band of 1–3 kHz and an angular range of ∼15–25° to obtain geoacoustic profiles and associated uncertainties, as well as frequency dependencies of sound speed and attenuation. The estimated geoacoustic profiles are similar to those from previous inversions of reflection-coefficient data at lower frequencies (0.4–1.3 kHz) collected at two different sites on the NEMP. Based on the inversion results at all three sites, a general interpretive model for sediment-column structure and variability is synthesized for the NEMP. This model includes an upper mud layer in which sediment properties change slightly with depth due to near-surface processes, an intermediate mud layer with uniform properties, and a transition layer where properties change rapidly with depth due to increasing sand content in the mud above a sand layer. [Work supported by the Office of Naval Research.]
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".