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Record W2149104678 · doi:10.1190/1.1993267

Underwater MASW to evaluate stiffness of water-bottom sediments

2005· article· en· W2149104678 on OpenAlexaff
Choon B. Park, Richard D. Miller, Jianghai Xia, Julian Ivanov, G V Sonnichsen, James A. Hunter, R. L. Good, R A Burns, H A Christian

Bibliographic record

VenueThe Leading Edge · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsNova Scotia Community CollegeGeological Survey of Canada
Fundersnot available
KeywordsUnderwaterGeologySeismologyOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Stiffness measurements are often necessary for geotechnical characterization of an underwater site. Seismically, these measurements can be made through the dispersion analysis of the Rayleigh-type surface waves. Successful terrestrial application of this method has been reported by many investigators using spectral analysis of surface waves (SASW) and more recently using multichannel analysis of surface waves (MASW). The MASW method was originally developed as a land survey method to investigate the near-surface materials for their elastic properties: for example, the shear-wave velocity (VS), by recording and analyzing Rayleigh-type surface waves using a vertical (impulsive) seismic source and receivers. The acquired data are first analyzed for dispersion characteristics and, from these the shear-wave velocity is estimated using an inversion technique.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.259
Teacher spread0.231 · 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

Citations82
Published2005
Admission routes1
Has abstractyes

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