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Record W6998473657

Acoustic Measurement of Sub-Aqueous Gravity-Driven Granular Flows

2023· article· en· W6998473657 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsGranular materialAttenuationAcoustic Doppler velocimetryTurbulenceFlow (mathematics)Reflection (computer programming)SedimentRange (aeronautics)Speed of soundFlow velocityFlow measurement
DOInot available

Abstract

fetched live from OpenAlex

Sub-aqueous granular flows are complex non-linear processes. In the ocean, the transport of granular materials at the bed depends on near-bed flow and turbulence as well as seafloor morphology and sediment properties. However, the feedbacks between these processes are still poorly understood in part due to the difficulty of making high resolution measurements without disturbing the near-bed flow. Acoustic remote-sensing technologies present a promising approach, being capable of achieving mm-scale range resolutions using MHz frequencies. However, to correctly interpret results, the interaction of MHz frequency sound with water-submerged sand-sized sediments must be understood. The first part of this presentation focuses on measurements of the geoacoustic properties of water-saturated sediment at high frequencies: sound speed and attenuation within the sediment and the reflection coefficient at the sediment-water interface. The second part focuses on sub-aqueous gravity-driven granular flows. The granular materials - 0.40 mm sand and 0.34 mm glass beads - were released from a cofferdam in a 15-cm wide inclined channel, producing O(1) cm thick granular layers moving at O(10) cm/s speeds. Vertical velocity profiles within the moving layer were measured using pulse-coherent Doppler profilers operating at MHz frequencies. Results are compared to velocity profiles measured by a side-view camera through the chute sidewall and surface velocity profiles across the chute measured by a top-view camera. Estimates of depth-averaged solids concentration are obtained from the best-fit parameters of a theoretical viscous flow model and compared to the measured concentration based on the attenuation of sound through the moving layer. Presenter Bio Dr. Jenna Hare is a Postdoctoral Research Associate at the Center for Coastal and Ocean Mapping at the University of New Hampshire working with Dr. Tony Lyons. Her research interests lie at the intersection of the fields of physical oceanography and underwater acoustics. Dr. Hare completed her BSc (2010) in physics at Laval University in Quebec followed by her MSc (2013) and PhD (2021) in oceanography at Dalhousie University in Nova Scotia.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.034
GPT teacher head0.206
Teacher spread0.173 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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