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Record W2799287242 · doi:10.1121/1.5035794

Geoacoustic inversion of the New England Mud Patch using modal time-frequency dispersion

2018· article· en· W2799287242 on OpenAlexaff
Julien Bonnel, Dimitrios Eleftherakis, Stan E. Dosso, N. Ross Chapman, David R. Dall’Osto, Peter H. Dahl

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAcousticsHydrophoneModal dispersionGeologyInversion (geology)Acoustic dispersionModalFrequency domainUnderwaterBroadbandTime domainImage warpingDispersion (optics)SeismologyPhysicsComputer scienceOpticsMaterials scienceAcoustic waveOceanography

Abstract

fetched live from OpenAlex

This paper presents geoacoustic inversion of several low-frequency broadband signals recorded during the Seabed Characterization Experiment (SBCEX) that took place on the New England Mud Patch in March 2017. The considered sources are chirps emitted by a towed J15 source and underwater impulses created by MK64 explosions. These sources are low-frequency (f<250 Hz) so that the shallow water environment acts as a dispersive waveguide, and propagation is conveniently described by modal theory. The received signal is recorded on a single hydrophone placed 0.8 m off the bottom. In this context, inversion is carried out by matching modal dispersion curves in the time-frequency domain. Experimental dispersion curves are estimated using a non-linear sampling scheme called warping. Up to six modes can be estimated, and non-linear inversion results are consistent with what is known about the area.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.245
Teacher spread0.227 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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