MétaCan
Menu
Back to cohort
Record W2391741542

Slope Stability Evaluation Based on Choquet Fuzzy Integral

2014· article· en· W2391741542 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

VenueWater power · 2014
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Decision-Making Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsChoquet integralFuzzy logicStability (learning theory)MathematicsAnalytic hierarchy processFuzzy measure theoryHierarchyFuzzy setSet (abstract data type)Process (computing)Mathematical optimizationApplied mathematicsFuzzy numberComputer scienceOperations researchArtificial intelligenceMachine learning
DOInot available

Abstract

fetched live from OpenAlex

The stability of slope system is affected by so many factors and there is a complex non-linear relationship among these factors. The slope stability evaluation model based on Choquet fuzzy integral is established, in which, some reasonable indexes are selected and the standard evaluation values are set, and then the evaluation weight is calculated based on expert judgment and analytical hierarchy process, and finally the evaluation information is integrated by Choquet fuzzy integral. The case study indicates that the model can make the physical concept very clear, the modeling process is visualized and convenient, and the analysis result is also reliable.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.025
GPT teacher head0.296
Teacher spread0.271 · 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