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Record W4229834646 · doi:10.1680/jgeot.16.p.304

Mechanical properties of a carbonate sand from a dredged hydraulic fill

2017· article· en· W4229834646 on OpenAlexaff
Daniela Giretti, V. Fioravante, K. Been, Stephen E. Dickenson

Bibliographic record

VenueGéotechnique · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsCarbonateGeotechnical engineeringGeologySubmarine pipelineOffshore geotechnical engineeringCarbonate rockMaterials science

Abstract

fetched live from OpenAlex

This paper focuses on the properties of a crushable, uncemented, carbonate sand, retrieved from an artificial island to be used for petroleum production offshore of the United Arab Emirates. The sand was dredged from an offshore borrow area, placed hydraulically within engineered bunds to create the island and densified by vibroflotation. The results of a comprehensive laboratory testing programme are summarised in this paper, providing a set of mechanical properties for carbonate sand. The laboratory programme included static, cyclic and dynamic tests, the results of which are interpreted in a critical state soil mechanics framework, including stress dilatancy. The results of this testing programme contribute to on-going research on the behaviour of carbonate sands under static and cyclic loading conditions. The results are also compared with selected data for other carbonate and siliceous sands, and highlight the behaviour of crushable, carbonate sands used as hydraulic fills.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.195
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations70
Published2017
Admission routes1
Has abstractyes

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