MétaCan
Menu
Back to cohort
Record W2058371619 · doi:10.1121/1.2785812

Modeling scattering from azimuthally symmetric bathymetric features using wavefield superposition

2007· article· en· W2058371619 on OpenAlexaff
John A. Fawcett

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsSuperposition principleScatteringBathymetryGeologyComputationSonarMathematical analysisGeometryAcousticsPhysicsOpticsMathematicsAlgorithm

Abstract

fetched live from OpenAlex

In this paper, an approach for modeling the scattering from azimuthally symmetric bathymetric features is described. These features are useful models for small mounds and indentations on the seafloor at high frequencies and seamounts, shoals, and basins at low frequencies. A bathymetric feature can be considered as a compact closed region, with the same sound speed and density as one of the surrounding media. Using this approach, a number of numerical methods appropriate for a partially buried target or facet problem can be applied. This paper considers the use of wavefield superposition and because of the azimuthal symmetry, the three-dimensional solution to the scattering problem can be expressed as a Fourier sum of solutions to a set of two-dimensional scattering problems. In the case where the surrounding two half spaces have only a density contrast, a semianalytic coupled mode solution is derived. This provides a benchmark solution to scattering from a class of penetrable hemispherical bosses or indentations. The details and problems of the numerical implementation of the wavefield superposition method are described. Example computations using the method for a simple scattering feature on a seabed are presented for a wide band of frequencies.

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: none
Teacher disagreement score0.632
Threshold uncertainty score0.547

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207