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Record W2326590408 · doi:10.1193/101914eqs164m

Amplification Effects of Thin Soft‐Surface Layers

2016· article· en· W2326590408 on OpenAlexaff
W. D. Liam Finn, Francisco Ruz

Bibliographic record

VenueEarthquake Spectra · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShakeBoreholeOutcropGeologySurface layerGeotechnical engineeringLayer (electronics)Magnitude (astronomy)Soil horizonSeismologyMineralogySoil scienceGeomorphologySoil waterMaterials scienceComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

The amplification effects of shallow soft‐surface layers with respect to an underlying hard‐soil layer or rock are studied using data recorded from the 2011 Tohoku earthquake. Twenty‐four sites have been studied with depths to rock ranging from 4–30 m. At each site, two records were available: one in rock at the bottom of the borehole at depths of 100–500 m and one on the surface of the soft‐soil layer. Analyses of the soil‐rock columns were conducted using the program SHAKE. Good agreement was found between calculated and recorded surface spectra, which demonstrated the reliability of SHAKE analyses for the sites under study. Therefore, SHAKE analyses were used to determine the outcrop motions at the top of rock. Amplification factors were determined by comparing characteristics of the surface and outcrop motions such as Fourier and response spectra. Computed amplification factors were correlated with V S 30 , V soil , and soft layer period, T = 4 H / V soil . The results show clearly that the most reliable correlations for estimating the amplification of soft shallow surface layers less than 30 m are those based on V S 30 and soft layer period, T .

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
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)0.0020.001

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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 designObservational
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

Citations12
Published2016
Admission routes1
Has abstractyes

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