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Record W2034578171 · doi:10.1190/1.1816368

Compressional and shear wave velocities for artificial granular media under simulated near surface conditions

2001· article· en· W2034578171 on OpenAlexaboutno aff
Brian P. Bonner, Patricia A. Berge, D. Wildenschild

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
FundersLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsShear (geology)Surface waveMaterials scienceMechanicsSurface (topology)PhysicsOpticsComposite materialGeometryMathematics

Abstract

fetched live from OpenAlex

Laboratory ultrasonic experiments were made on artificial soil samples in order to observe the effects of slight overburden, sand/clay ratio and pore fluid saturation on compressional and shear wave velocities. Up to several meters of overburden were simulated by applying low uniaxial stress of about 0.1 MPa to a restrained sample. Samples were fabricated from Ottawa sand mixed with a swelling clay (Wyoming bentonite). The amount of clay added was 1 to 40 percent by mass. Most measurements were made under room-dry conditions, but some measurements were made for fully-saturated sand-clay mixtures and for partially-saturated sand samples. For the dry sand-clay samples, compressional (P) velocities were low, ranging from about 200 to 500 m/s for the mixtures at low stress. Shear (S) velocities were about half of the compressional velocity, about 70 to 250 m/s. Dramatic increases in all velocities occurred with small uniaxial loads, indicating strong nonlinearity. Composition and grain packing control the mechanical response at grain contacts and the resulting nonlinear response at low stresses. P and S velocities are sensitive to the amount of clay added, even at low concentrations. At these low equivalent overburden conditions, adhesion and capillarity at grain contacts affect wave amplitudes, velocities, and frequency content in the partial saturation case.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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)0.0050.000

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 head0.039
GPT teacher head0.245
Teacher spread0.206 · 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.

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

Citations9
Published2001
Admission routes1
Has abstractyes

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