MétaCan
Menu
Back to cohort
Record W2158783400 · doi:10.1061/9780784479087.178

The Random Finite Element Method (RFEM) in Probabilistic Slope Stability Analysis with Consideration of Spatial Variability of Soil Properties

2015· article· en· W2158783400 on OpenAlex
Pooya Allahverdizadeh, D. V. Griffiths, Gordon A. Fenton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIFCEE 2015 · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFinite element methodProbabilistic logicStability (learning theory)Reliability (semiconductor)Parametric statisticsRandom variableProbabilistic analysis of algorithmsSlope stabilitySlope stability analysisFocus (optics)Geotechnical engineeringMathematicsComputer scienceStatisticsStructural engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

This paper presents the results of probabilistic analyses in slope stability problems using the Random Finite Element Method (RFEM). The influence of spatially variable soil properties on design outcomes relating to slope stability analysis has been assessed through parametric studies, with focus on the “worst case” (critical) spatial correlation length that leads to a minimum reliability of the soil mass. This critical value is of particular interest, because it could be used for design in the absence of good site specific 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.

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)0.003
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 score0.688
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.232
Teacher spread0.210 · 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