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Record W1972747844 · doi:10.1121/1.2935860

On the angular dependence of sound scattering from natural sand in turbulent suspensions

2008· article· en· W1972747844 on OpenAlexaff
Alex E. Hay, Stephanie A. Moore, Wesley Paul

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsScatteringJet (fluid)OpticsAttenuationPhysicsTurbulenceAcousticsCross section (physics)Range (aeronautics)Materials scienceBistatic radarComputational physicsMechanicsRadarTelecommunications

Abstract

fetched live from OpenAlex

We report on measurements of the variation with scattering angle of the differential scattering cross section of particles embedded in turbulence. The experimental apparatus consists of a turbulent round water jet carrying the particles, and a pair of broadband MHz-frequency transducers operating as transmitter and receiver and arranged in a bistatic geometry centred on the jet axis. The purpose of the jet is to confine the scatterers within a restricted range interval and thereby eliminate the need to correct the signal for attenuation due to scattering along the transmit and receive paths. Measurements were made at scattering angles between 90 and 170 degrees, for both natural sand grains and spherical glass beads in narrow size distributions with median diameters ranging from 200 to 500 μm. Particle size distributions were determined using different methodologies to obtain both volumetric and projected area estimates of equivalent spherical size. The results are compared to the theory for scattering by a solid sphere. Implications regarding the choice of equivalent sphere size for sound scattering by natural sand grains in suspension are discussed.

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)0.001
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
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.222
Teacher spread0.211 · 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
Published2008
Admission routes1
Has abstractyes

Explore more

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