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Record W3210861804 · doi:10.32920/ryerson.14644542.v1

Applications of ground penetrating radar in geotechnical investigation

2021· preprint· en· W3210861804 on OpenAlexaff
Damian Moodie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGround-penetrating radarBoreholeGeotechnical investigationGeotechnical engineeringGeologyRadarRemote sensingEngineering

Abstract

fetched live from OpenAlex

There are clearly risks and a fair degree of uncertainties involved in geotechnical investigation for the reason that only limited boreholes can be used in projects, due to budget restraints. These risks are further increased or decreased subject to the geotechnical engineers’ experiences and judgments. Ground Penetrating Radar (GPR) is a geophysical technique that provides continuous non-destructive soil profiling from the surface or from inside a borehole by sending, receiving and averaging multiple radio wave pulses into the subsurface at centimeter increments (cm) scale normally ranging between 0.5cm to 1cm step size. This project focuses on the principles, procedures, applications and limitations of GPR use in geotechnical exploration. To evaluate its potentials for reducing risk and uncertainties associated with soil profile presumptions between boreholes, also to evaluate if GPR can provide objective quantifiable data that can be understood by any level of geotechnical engineers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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