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Record W2999517711 · doi:10.1061/9780784481486.031

Initial Observations on Laboratory Shear Loading Response of Sand-Silt Mixtures

2018· article· en· W2999517711 on OpenAlexafffund
Achala Nishan Soysa, Dharma Wijewickreme

Bibliographic record

VenueGeotechnical Earthquake Engineering and Soil Dynamics V · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSiltGeotechnical engineeringGeologySlurryShear (geology)Shear strength (soil)LoessCompression (physics)Materials scienceSoil waterComposite materialSoil scienceGeomorphology

Abstract

fetched live from OpenAlex

An experimental research program has been undertaken to investigate the monotonic shear loading response of sand-silt mixtures covering the complete range of sand-silt compositions in the mixtures. Initial observations and findings from the undrained monotonic triaxial compression and extension testing, as well as from drained triaxial compression tests, performed on non-plastic Fraser River silt, Fraser River sand, and 50%-50% mixture of the same two materials, are presented. The specimens of Fraser River sand were prepared using water-pluviation, and specimens of silt and sand-silt mixtures were prepared via slurry deposition. The results presented provide an initial insight to the complex behavioral patterns emerging from drained, undrained, compression, and extension loading responses, under some select sand and silt compositions. The findings illustrate the variation of the key soil parameters such as stiffness, shear strength/friction angle as the soil transitions from a coarse-grained to fine-grained matrix.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.009
GPT teacher head0.210
Teacher spread0.200 · 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 designSimulation or modeling
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

Citations0
Published2018
Admission routes2
Has abstractyes

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