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Record W2755381535 · doi:10.1080/17499518.2017.1376253

Spatial distribution of soil shear-wave velocity and the fundamental period of vibration – a case study of the Saguenay region, Canada

2017· article· en· W2755381535 on OpenAlexafffundabout
Thomas Foulon, Ali Saeidi, Romain Chesnaux, Miroslav Nastev, Alain Rouleau

Bibliographic record

VenueGeorisk Assessment and Management of Risk for Engineered Systems and Geohazards · 2017
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsCentre de Géomatique du QuébecUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Chicoutimi
KeywordsBedrockGeologySpatial distributionShear (geology)SeismologyMicrotremorVibrationWave velocitySpatial variabilityShear velocityGeomorphologyMeteorologyGeographyTurbulenceRemote sensingPaleontologyPhysics

Abstract

fetched live from OpenAlex

The spatial distribution of soil shear-wave velocity and the fundamental period of vibration were selected as input parameters for the determination of potential seismic site effects in the Saguenay region, Canada. The methodology used in this study involved three clear steps. First, a 3D geological model of the surficial deposits was built taking into consideration the type, spatial distribution and thickness of the deposits. Second, representative average Vs values were determined for each of the major soil units. Finally, the average shear-wave velocity from the ground surface to bedrock (Vsav), the shear-wave velocity of the upper 30 m (Vs30) and the fundamental site resonance period (T0) were calculated over a regular grid for the study area. The results include the spatial distribution of the fundamental site resonance period, the average shear-wave velocity in the first 30 m of the ground and the spatial distribution of National Building Code of Canada seismic soil classes for the Saguenay region.

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 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 score0.308
Threshold uncertainty score0.981

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)0.0000.000

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 head0.010
GPT teacher head0.225
Teacher spread0.216 · 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

Citations16
Published2017
Admission routes3
Has abstractyes

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