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Record W2769438884 · doi:10.1121/1.5014904

Limitations of a Gaussian Beam Model for low-frequency acoustic propagation under ice

2017· article· en· W2769438884 on OpenAlexaff
Sean Pecknold, Diana F. McCammon, Dale D. Ellis

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsAcousticsGaussian beamReflection (computer programming)AmplitudeUnderwaterUnderwater acousticsBeam (structure)GaussianWavenumberRange (aeronautics)Reflection coefficientOpticsGeologyPhysicsComputer scienceMaterials science

Abstract

fetched live from OpenAlex

Ray-based propagation models are often used to simulate underwater acoustic communications signals. For longer-range low frequency acoustic communications, particularly under ice cover, it is necessary to determine if a ray-based model can give accurate results. An elastic ice reflection algorithm was added to the Bellhop Gaussian beam model, and the results for mid- and low-frequency propagation were compared to results obtained using a normal modes model adapted for an elastic ice reflection coefficient, and to the OASES wavenumber integration model. The effects of simplified boundary conditions, frequency, and ice thickness on amplitude and phase matching are examined.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.224

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.263
Teacher spread0.209 · 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
GenreMethods

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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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