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Record W2621116261 · doi:10.1061/9780784480724.007

Probabilistic Calibration of a Modified Van der Poel Model Representing the Viscoelastic Behavior of Sandstone

2017· article· en· W2621116261 on OpenAlexaff
Yichuan Zhu, Cong Lu, Xinli Hu, Zenon Medina‐Cetina

Bibliographic record

VenueGeo-Risk 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsViscoelasticityCalibrationProbabilistic logicComputer scienceArtificial intelligenceMaterials scienceMathematicsComposite materialStatistics

Abstract

fetched live from OpenAlex

The study of rheological behavior of rocks is fundamental to the interpretation of long-term time-dependent deformations and of fracturing processes. This work introduces the probabilistic calibration method to account for the uncertainty resulting from the sources of evidence used to conduct a model calibration (i.e., experimental observations, model prediction and expert’s judgment). Conditioned on a series of triaxial compression creep experiments conducted on sandstone, this research introduces a systematic methodology through the probabilistic assessment of a modified Van der Poel model to simulate the viscoelastic behavior of a sandstone specimen under a single loading condition. Results show the structural correlation (linear and non-linear) between the model parameters, and the effects they pose on the rheological behavior of the specimen.

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 score0.023
Threshold uncertainty score0.407

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 head0.026
GPT teacher head0.256
Teacher spread0.230 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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