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Record W4409148033 · doi:10.1121/10.0036374

Bayesian matched-field inversion for shear and compressional geoacoustic profiles at the New England Mud Patch

2025· article· en· W4409148033 on OpenAlexafffund
Stan E. Dosso, Preston S. Wilson, David P. Knobles, Julien Bonnel

Bibliographic record

VenueJASA Express Letters · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsAttenuationGeologyShear (geology)Longitudinal waveInversion (geology)SeismologyWave propagationPetrologyOpticsPhysics

Abstract

fetched live from OpenAlex

This Letter estimates shear and compressional seabed geoacoustic profiles at the New England Mud Patch through trans-dimensional Bayesian inversion of matched-field acoustic data over a 20-2000 Hz bandwidth. Results indicate low shear-wave speeds (∼35 m/s) with relatively small uncertainties over most of the upper mud layer, increasing in underlying transition and sand layers. Compressional parameters, including attenuation, are also well estimated, but shear-wave attenuation is poorly determined. Comparison of inversions with/without shear parameters and consideration of inter-parameter correlations indicate that estimates of compressional parameters are not substantially influenced by shear effects, with the possible exception of compressional-wave attenuation in the sand layer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.242
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2025
Admission routes2
Has abstractyes

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