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Record W4236836321 · doi:10.1121/1.4799119

The spatial properties of breaking wave generated and bedload transport generated noise in the sediment layer of a shallow water wave guide.

2013· article· en· W4236836321 on OpenAlexaff
David R. Barclay, Len Zedel, Alex E. Hay, Mathew G. Hatcher

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsBed loadGeologyAmbient noise levelSediment transportNoise (video)SedimentBreaking waveGeomorphologyWave propagationPhysicsOpticsSound (geography)

Abstract

fetched live from OpenAlex

In May of 2012, three weeks of ambient noise measurements from a hydrophone buried 30 cm deep in the sediment were recorded at Advocate Beach, a 1:10 sloped beach at the head of the Bay of Fundy, Nova Scotia. While tides varied the mean water depth between 0 and 4 m, 0.8 m surface waves passed overhead, driving sediment bedload transport and creating an ambient noise field in the sediment consisting of two primary components: noise generated by bubbles formed in breaking waves at the surface and noise generated by the collisions of sand, gravel and cobble in the bedload transport along the seabed. Both of these noise sources are stochastic and can be described by their second order statistics: power spectral density, spatial coherence and directional density. In an effort to distinguish these two noise sources, the spatial properties of three full wave models of the noise field in the sediment are compared, using an infinite sheet of sources placed near the surface of a Pekeris waveguide to model breaking wave noise, near the fluid-fluid interface of a Pekeris waveguide to model bedload transport noise, or near the fluid-fluid interface of two infinite half-spaces to model bedload transport noise.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.708

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 science0.0000.000
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 head0.031
GPT teacher head0.221
Teacher spread0.190 · 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 designBench or experimental
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

Citations1
Published2013
Admission routes1
Has abstractyes

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