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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 OpenAlex
Damian Moodie

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.626

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 head0.022
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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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