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Record W2186202859 · doi:10.1139/cgj-2014-0068

Estimating slope of critical state line from cone penetration test — an update

2014· article· en· W2186202859 on OpenAlexvenueno aff
David Reid

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsGradationCone penetration testCompressibilityGeotechnical engineeringPenetration testSoil testPenetration (warfare)MathematicsSoil scienceGeologyStatisticsSoil waterMechanicsPhysicsSubgradeComputer science

Abstract

fetched live from OpenAlex

Estimation of density from cone penetration test (CPT) results is challenging, as cone resistance is a function of a number of soil properties, particularly compressibility. While many of the relevant soil properties can be determined from laboratory testing of recovered samples, this process is not feasible when investigating a deposit of soil with significant variations in gradation and (or) properties across depth and lateral extent. To provide a first-order estimate of density from CPT results alone, a number of correlations have been developed to ascertain other soil properties, particularly compressibility, from CPT data. In particular, friction ratio and soil behaviour type index have been suggested as providing indications of soil compressibility, referenced as the slope of the critical state line. Two of these correlations are critically assessed through the identification and analysis of data from 31 sites where both CPT and relevant laboratory testing were conducted. This process indicates that while the additional data analyzed in this study are generally consistent with previous correlations in terms of trend, significant scatter is evident. In addition, the difficulty in directly relating recovered samples to specific CPT data is outlined.

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 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: none
Teacher disagreement score0.814
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.213
Teacher spread0.204 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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