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Record W2038369443 · doi:10.1121/1.4757970

Mid frequency shallow water fine-grained sediment attenuation measurements

2013· article· en· W2038369443 on OpenAlexaff
Charles W. Holland, Stan E. Dosso

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
FundersOffice of Naval Research
KeywordsAttenuationReverberationWaves and shallow waterSedimentGeologyAcousticsRange (aeronautics)SiltMaterials scienceOpticsPhysicsOceanographyGeomorphology

Abstract

fetched live from OpenAlex

Attenuation is perhaps the most difficult sediment acoustic property to measure, but arguably one of the most important for predicting passive and active sonar performance. Measurement techniques can be separated into "direct" measurements (e.g., via sediment probes, sediment cores, and laboratory studies on "ideal" sediments) which are typically at high frequencies, O(10(4)-10(5)) Hz, and "indirect" measurements where attenuation is inferred from long-range propagation or reflection data, generally O(10(2)-10(3)) Hz. A frequency gap in measurements exists in the 600-4000 Hz band and also a general acknowledgement that much of the historical measurements on fine-grained sediments have been biased due to a non-negligible silt and sand component. A shallow water measurement technique using long range reverberation is critically explored. An approximate solution derived using energy flux theory shows that the reverberation is very sensitive to depth-integrated attenuation in a fine-grained sediment layer and separable from most other unknown geoacoustic parameters. Simulation using Bayesian methods confirms the theory. Reverberation measurements across a 10 m fine-grained sediment layer yield an attenuation of 0.009 dB/m/kHz with 95% confidence bounds of 0.006-0.013 dB/m/kHz. This is among the lowest values for sediment attenuation reported in shallow water.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.591
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.250
Teacher spread0.218 · 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.

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

Citations12
Published2013
Admission routes1
Has abstractyes

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