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Record W3031644349 · doi:10.1520/gtj20180205

Piezoelectric Ring-Actuator Technique: In-Depth Scrutiny of Interpretation Method

2020· article· en· W3031644349 on OpenAlexaff
Mahmoud N. Hussien, Mourad Karray

Bibliographic record

VenueGeotechnical Testing Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsScrutinyGeotechnical engineeringInterpretation (philosophy)ActuatorGeologyPiezoelectricityRing (chemistry)Structural engineeringMaterials scienceEngineeringComputer scienceComposite materialLawElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The piezoelectric ring-actuator technique (P-RAT) is a recent laboratory technique for the measurement of shear wave velocity (Vs) of soils. The measurement is based on transmission of a mechanical signal through the soil specimen with source and receiver transducers capsulated, for instance, in the end platens of an oedometer cell. An interpretative framework of the signals produced in the P-RAT has also been developed to minimize the subjectivity of the process and provide a consistent approach to Vs determination. However, there are some issues that would potentially affect the quality of the signals; consequently, biases of the velocity determination have not yet been explicitly discussed, such as the effects of sample and sensor characteristics as well as the signals used to excite the P-RAT sensors. P-RAT experiments on two different soils using different sensors, input signals, and oedometer cells are implemented in this study. The main purpose of the tests is not to elicit definitive information about the tested materials but to allow the P-RAT interpretation methodology to be revealed. The results suggest that this interpretation method can be beneficially used with other piezoelectric techniques (e.g., BE).

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)0.001
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.025
GPT teacher head0.252
Teacher spread0.227 · 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
GenreMethods

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

Citations10
Published2020
Admission routes1
Has abstractyes

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