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Record W6925226768 · doi:10.17603/ds2-r49h-2j81

Effect of Relative Density on Cyclic Response of Ottawa F65 Sand

2025· dataset· en· W6925226768 on OpenAlexaboutno aff

Bibliographic record

VenueTexas Advanced Computing Center · 2025
Typedataset
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsnot available
Fundersnot available
KeywordsRelative densityLiquefactionShear (geology)Bulk densityShear stressStress pathDirect shear testShear strength (soil)

Abstract

fetched live from OpenAlex

A series of cyclic direct simple shear tests are conducted to investigate the effect of static shear stress on stress-strain-strength behavior of Ottawa F65 sand at three distinct relative densities (50%, 60%, and 90%). The new experimental data supplements the LEAP-2022 data (Lbibb and Manzari, 2023) in which the effects of overburden stress, relative density, and static shear stress on cyclic strength of Ottawa F65 sand were reported for relative densities of 55%, 66%, 71%, and 75%. The report provided along with the datasets presents the setup and experimental procedure, as well as the liquefaction strength curves and the results obtained from each test. The dataset presented in this report builds on the soil characterization tests, cyclic triaxial tests, and direct simple shear tests performed for the LEAP-2017, LEAP-2018/9, LEAP-2020 (ElGhoraiby, Park and Manzari, 2017, 2018a, b, 2020), and LEAP-2022 (Lbibb and Manzari, 2023). Along with the current dataset, archived on DesignSafe-CI, a MATLAB script is also provided for post-processing and visualization of the experimental data.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.186
Threshold uncertainty score0.371

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.004
GPT teacher head0.246
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2025
Admission routes1
Has abstractyes

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