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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.999

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)0.0020.003

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