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Record W2944402839 · doi:10.1139/cgj-2018-0698

Threshold fines content and behavior of sands with nonplastic silts

2019· article· en· W2944402839 on OpenAlexvenueno aff
Carmine P. Polito, Erin L. D. Sibley

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsSiltGeotechnical engineeringSoil waterWater contentLiquefactionMaterials scienceGeologySoil science

Abstract

fetched live from OpenAlex

The threshold fines content of a sand with nonplastic fines represents the silt content where the soil transitions from being a sand matrix, with silt particles entirely contained in the voids between the sands, to being a silt matrix that contains isolated sand grains. A laboratory testing program was performed on a series of mixtures of sand and silt, with silt contents ranging from 0% to 45%, to determine if the friction angle, cyclic resistance, and normalized dissipated energy per unit volume required to initiate liquefaction changed based upon the silt content of the soil relative to the threshold fines content. These data were evaluated with respect to whether the silt content of the specimens was below the lower-bound threshold fines content or above the upper-bound threshold fines content. It was determined that soils above the upper-bound threshold fines content had lower friction angles, lower cyclic resistances, and required less normalized dissipated energy per unit volume to initiate liquefaction than soils below the lower-bound threshold fines content. It was also shown that under the larger strains experienced during monotonic testing, the friction angle did not reach a constant value until it was well above the upper-bound limiting silt content.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.549

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 integrity0.0000.001
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 head0.010
GPT teacher head0.173
Teacher spread0.163 · 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 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

Citations31
Published2019
Admission routes1
Has abstractyes

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