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Record W2915399026 · doi:10.1139/cgj-2017-0738

Methods used to determine maximum and minimum dry unit weights of sand: Is there a need for a new standard?

2019· article· en· W2915399026 on OpenAlexvenueno aff
Tom Lunne, Siren Knudsen, Øyvind Blaker, T. Vestgården, J. J. M. Powell, C Wallace, L. Krogh, N.V. Thomsen, Gülin Luis Yetginer, R. K. Ghanekar

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringAcceptance testingDry weightMathematicsEnvironmental scienceGeologyEngineering

Abstract

fetched live from OpenAlex

Known challenges exist with maximum (γ dmax ) and minimum (γ dmin ) dry unit weight measurements; the respective dry unit weight results depend very much on the method or standard used. A laboratory testing programme was completed to systematically determine and compare γ dmax and γ dmin values derived for six different sand types by using different methods. The tested sands contained a wide variety of mineralogical and fines contents. The γ dmax and γ dmin determinations were performed according to the following methods: British Standards Institution (BS) standards; American Society for Testing and Materials (ASTM) standards; Deutsches Institut für Normung (DIN) standards; Dansk Geoteknisk Forening (DGF) guidelines; Norwegian Geotechnical Institute (NGI), Geolabs, and Fugro proprietary methods. Differences in testing procedures, material requirements for testing, and the effects of soil degradation during testing introduce challenges and large differences in γ dmax and γ dmin values for each of the six sand types were observed. Therefore, it is concluded that there is a need for the development of new standards for a robust determination of γ dmax and γ dmin values. Specifically, a standard for determining γ dmax is required to consistently obtain results at the upper bound of dry unit weight values for the likely range of sands — without crushing the sand grains.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.254
Teacher spread0.235 · 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 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

Citations37
Published2019
Admission routes1
Has abstractyes

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