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Record W2070608465 · doi:10.1121/1.4877303

Bill Carey and sound attenuation in marine sediments

2014· article· en· W2070608465 on OpenAlexaff
Ross Chapman

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCanyonAttenuationSound (geography)GeologySubmarine canyonSedimentContext (archaeology)Sound propagationAcousticsAcoustic attenuationOceanographyDispersion (optics)GeomorphologyPhysicsPaleontologyOptics

Abstract

fetched live from OpenAlex

Experiments carried out by Bill Carey in the Hudson Canyon off the New Jersey coast provided a great wealth of data for the study of low frequency sound propagation in shallow water. His analysis of the transmission loss data, reported in a series of papers dating from the mid 1990s, indicated a non linear frequency dependence of sound attenuation in the sediment material. This work provided a large base of experimental evidence for the non linear dispersion predicted by the Biot theory of sound propagation in porous media, and it stimulated new studies on sound propagation in marine sediments by many researchers, including Bill Carey himself. This paper reviews the results obtained by Carey from the Hudson Canyon experiments and places them in the context of new results from more recent experiments using different techniques and observables. Analysis of results from the new work indicates that Carey's observation of non linear frequency dependence in sediment material in the Hudson Canyon applies to attenuation of sound in different types of marine sediments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.252
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

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