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Record W3093580549 · doi:10.1121/10.0002250

Phase speed in water-saturated sand and glass beads at MHz frequencies

2020· article· en· W3093580549 on OpenAlexafffund
Jenna Hare, Alex E. Hay

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceRADIUSPorosityPhase (matter)Dispersion (optics)Speed of soundRange (aeronautics)Grain sizeComposite materialParticle (ecology)ScatteringGranular materialParticle sizeMineralogyOpticsPhysicsGeologyThermodynamics

Abstract

fetched live from OpenAlex

Measurements of the phase velocity of compressional sound waves in water-saturated granular materials are reported for the 1.0-2.0 MHz frequency range. The sound speed estimates are based on travel times through granular layer thicknesses ranging from 8 to 17 mm. Three types of granular media were used: 336 μm median diameter glass beads and two natural sands with median diameters of 219 and 406 μm. These grain sizes and frequency range correspond to 0.5<ka<1.2, where k is the wavenumber and a the grain radius. To remove trapped air, the samples were boiled under pressure before transfer to the measurement tank. The results are compared to previously reported experimental results and to the Schwartz and Plona [J. Appl. Phys. 55(11), 3971-3977 (1984)] multiple scattering prediction, confirming negative dispersion for ka > 0.5. Scaling the data by a factor depending on porosity and grain density reduces the spread among the available phase speed estimates by nearly a factor of 2, from 12.5% to 6.9%.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.532

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.265
Teacher spread0.236 · 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 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

Citations4
Published2020
Admission routes2
Has abstractyes

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