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Record W2090617046 · doi:10.1121/1.4900398

Modeling three dimensional environment and broadband acoustic propagation in Arctic shelf-basin region

2014· article· en· W2090617046 on OpenAlexaboutno aff
Mohsen Badiey, A. Muenchow, Lin Wan, Megan S. Ballard, David P. Knobles, Jason D. Sagers

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyArcticUnderwaterOceanographyContinental shelfCanada BasinAmbient noise levelWater columnSpeed of soundStructural basinSound (geography)The arcticEnvironmental scienceAcousticsGeomorphologyPhysics

Abstract

fetched live from OpenAlex

Rapid climate change over the last decade has created a renewed interest in the nature of underwater sound propagation in the Arctic Ocean. Changes in the oceanography and surface boundary conditions are expected to cause measurable changes in the propagation and scattering of low frequency sound. Recent measurements of a high-resolution three-dimensional (3-D) sound speed structure in a 50 x 50 km2 region in an open-water shelf-basin region of the Beaufort Sea offer a unique and rare opportunity to study the effects of a complex oceanography on the acoustic field as it propagates from the deep basin onto the continental shelf. The raw oceanography data were analyzed and processed to create a 3-D sound speed field for the water column in the basin-slope-shelf area. Recent advances in both 2-D and 3-D acoustic modeling capability allow one to study the effects of the range- and azimuth-dependent water column layers on the frequency-dependent acoustic modal structure. Of particular interest is the nature of the 3-D and mode-coupling effects on the frequency response induced by the oceanography. The results will likely be useful in designing acoustic experiments with serious logistical constraints in the rapidly changing Arctic Ocean.

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 score0.762
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.222
Teacher spread0.204 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

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