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Record W2096053016 · doi:10.1139/t00-060

Using time domain reflectometry in triaxial testing

2000· article· en· W2096053016 on OpenAlexfundvenueno aff
J LH Grozic, Michel Elzear. Lefebvre, P. K. Robertson, N. R. Morgenstern

Bibliographic record

VenueCanadian Geotechnical Journal · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsReflectometryDegree of saturationGeotechnical engineeringTime domainVoid ratioSaturation (graph theory)Water contentSoil waterGeologySoil scienceMathematicsComputer science

Abstract

fetched live from OpenAlex

Time domain reflectometry (TDR) can be used to determine the volumetric water content of soils. This note describes the utilization of a TDR miniprobe in triaxial testing. The TDR performance was examined with a series of tests that not only proved its reliability but also resulted in two empirical correlations. Using these correlations, the degree of saturation and volumetric water content during triaxial testing could be determined. The TDR was then put to use in a laboratory program designed to investigate the response of loose gassy sand under static and cyclic loading. Because of the TDR measurements it was possible to determine the degree of saturation and void ratio of the gassy specimens. The TDR miniprobe proved to be accurate, simple to use, and inexpensive to build.Key words: time domain reflectometry, TDR, triaxial testing, gassy, unsaturated.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.254
Teacher spread0.229 · 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 designOther design
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

Citations3
Published2000
Admission routes2
Has abstractyes

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