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Record W4237865220 · doi:10.1121/1.4800718

Analysis of the vertical structure of deep ocean noise using measurements from the SPICEX and PhilSea experiments

2013· article· en· W4237865220 on OpenAlexaff
Kathleen E. Wage, Mehdi Farrokhrooz, Matthew A. Dzieciuch, Peter F. Worcester

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversité de Montréal
FundersOffice of Naval Research
KeywordsAmbient noise levelNoise (video)GeologyAcousticsWater columnNoise measurementFocus (optics)Deep waterWind speedNoise reductionSound (geography)Computer scienceOceanographyPhysicsOpticsArtificial intelligence

Abstract

fetched live from OpenAlex

There are open research questions about the vertical structure of low-frequency ambient noise in deep water.For example Gaul et al.'s [IEEE JOE, 2007] analysis of the Church Opal data set showed that noise decreases substantially (on the order of 20 dB) below the critical depth, whereas other researchers have reported more modest reductions [Morris, JASA, 1978].Two deep water experiments provided a unique opportunity to measure ambient noise using large vertical arrays.In 2004-2005 SPICEX used two arrays to sample a North Pacific environment.One array was centered on the sound channel axis, and the other array had hydrophones above and below the critical depth.In 2010-2011, the PhilSea experiment deployed a single array with 150 hydrophones spanning the full water column.Both experiments made repeated short measurements (each 2-3 minutes long) of the field at the arrays.This talk compares the ambient noise observed during SPICEX and PhilSea with results reported in the literature.Since these data sets contain receptions over the period of a year, we focus on the seasonal dependence of the noise field.In addition to investigating noise level as a function of depth, we consider wind dependence and vertical directionality.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.034
GPT teacher head0.257
Teacher spread0.223 · 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 designObservational
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

Citations7
Published2013
Admission routes1
Has abstractyes

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