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Record W2320462187 · doi:10.1061/40914(233)19

Traditional and Advanced Probabilistic Slope Stability Analysis

2007· article· en· W2320462187 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSlope stabilityReliability (semiconductor)Probabilistic logicSafety factorStability (learning theory)Slope stability analysisMeasure (data warehouse)Factor of safetyFinite element methodCorrelationRandom variableShear strength (soil)Benchmark (surveying)MathematicsGeotechnical engineeringStructural engineeringGeologyStatisticsComputer scienceEngineeringGeometryData miningPhysicsGeodesyPower (physics)

Abstract

fetched live from OpenAlex

The paper contrasts results obtained by the traditional First Order Reliability Method (FORM) and a more advanced Random Finite Element Method (RFEM) in a benchmark problem of slope stability analysis with random shear strength parameters. The key difference between the methods is that RFEM takes into account spatial correlation in a rigorous way allowing slope failure to occur naturally along the path of least resistance. Both methods lead to predictions of the "probability of slope failure" as opposed to the more traditional "factor of safety" measure of slope safety, however they give significant different results depending on the value of the correlation length. For small correlation lengths FORM is generally conservative, however it is shown that there is a "worst case" correlation length for which FORM leads to unconservative predictions of slope reliability.

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)0.000
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: none
Teacher disagreement score0.540
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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

Quick stats

Citations47
Published2007
Admission routes1
Has abstractyes

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