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Record W2981842028 · doi:10.4095/299413

Shear wave velocity estimation from piezocone test data for eastern Canada sands (Quebec and Ontario) - extended version with appendices

2016· report· en· W2981842028 on OpenAlexaffabout
Didier Perret, E Charrois, Manon Bolduc

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsWave velocityGeologyShear (geology)SeismologyGeotechnical engineeringTest (biology)GeodesyPaleontology

Abstract

fetched live from OpenAlex

Different relations between shear wave velocity and parameters obtained from seismic cone penetration tests are evaluated against a data set collected at 107 sites along the St. Lawrence River Valley in Eastern Canada. Only sands or sand-like soils with a normalized SBT index Ic less than 2.60 and a normalized porewater pressure ratio Bq less than 0.10 are considered in this study. All investigated soils are approximately 7,000-12,000 years old and are of a marine, mainly deltaic, origin. Correlations established for Holocene sands systematically under-predict shear wave velocities determined for the tested soils while the opposite is observed for correlation developed for Pleistocene sands. Non-linear regressions and residual analyses conducted for the tested soils indicate that the best prediction model has a functional form incorporating the cone tip resistance, the sleeve friction resistance, and the effective vertical overburden stress. A good correlation is also obtained with only the cone tip resistance and depth. As a result, two new equations are presented specifically for sands in the St. Lawrence River Lowlands which allow for an estimation of shear wave velocity based on piezocone test data. This open-file report is an extended version of a paper presented at the 69th Canadian Geotechnical Conference in Vancouver (Perret et al., 2016).

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 score0.466
Threshold uncertainty score0.996

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)0.0050.000

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 head0.033
GPT teacher head0.227
Teacher spread0.194 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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