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Record W4285269706 · doi:10.11159/ijci.2022.009

Numerical Analysis of CPT Results for Thin Layer and Transition Effects

2022· article· en· W4285269706 on OpenAlexvenueno aff
Houman Soleimani Fard, Nicol Ed-Chang

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2022
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsTransition (genetics)Layer (electronics)Materials scienceChemistryComposite material

Abstract

fetched live from OpenAlex

(especially the cone resistance, ) are not only a function of the properties of the soil in which the cone is located, but also the layers ahead and behind that. in this paper, using series of numerical simulations, variations of in a multiple-layered soil with sharp borders were studied. The model consisted of a 50-to 300-mm-thick layer of soft fine grained soil embedded in dense coarse grained layers. The ratio of the moduli of elasticity of the soft to dense soils, , ranged from 0.042 to 0.833. The transition zones (the distances above and below the soft layer over which the is affected) were analyzed as a function of and . Further, a method was introduced to capture and backcalculate the actual from the measured values.

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 score0.377
Threshold uncertainty score0.292

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 integrity0.0000.000
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 head0.007
GPT teacher head0.271
Teacher spread0.265 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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