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Record W2984425261 · doi:10.1016/j.soildyn.2019.105906

Blind comparison of non-invasive shear wave velocity profiling with invasive methods at bridge sites in Windsor, Ontario

2019· article· en· W2984425261 on OpenAlexafffundabout
Alex Bilson Darko, Sheri Molnar, Abouzar Sadrekarimi

Bibliographic record

VenueSoil Dynamics and Earthquake Engineering · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsWestern University
FundersGovernment of Alberta Ministry of Transportation
KeywordsBedrockWindsorWave velocityGeologySeismologyBoreholeMicrotremorShear (geology)Geotechnical engineeringSoil scienceGeomorphologyPetrology

Abstract

fetched live from OpenAlex

Canadian seismic design guidelines classify subsurface ground conditions based on the average shear-wave velocity (VS) of the upper 30 m (VS30). We seek to optimize a robust earthquake site classification procedure for Ontario bridge sites, assessed primarily from blind comparison of non-invasive and invasive shear-wave velocity (VS) depth profiling techniques. Non-invasive seismic testing is performed at 6 bridge sites in Windsor, Ontario co-located with invasive penetration and/or borehole VS measurements. Non-invasive surface wave dispersion and site amplification functions are jointly inverted to retrieve VS profiles at each site. Bridge sites tested are found to be mostly characterized with sediments up to ~30 m thick overlying seismic bedrock. Excellent agreement of VS30 estimates is obtained between both invasive and non-invasive methods and we notably determine an overall average relative difference in VS between methodologies of 9% for soil layers. Earthquake site classification based on VS is consistent at all sites regardless of methodology.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.985

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 head0.017
GPT teacher head0.233
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 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
Published2019
Admission routes3
Has abstractyes

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