MétaCan
Menu
Back to cohort
Record W2282463055 · doi:10.17736/ijope.2015.jc633

Probabilistic Lateral Buckling Assessment

2015· article· en· W2282463055 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

VenueInternational Journal of Offshore and Polar Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsBucklingProbabilistic logicMonte Carlo methodStructural engineeringFinite element methodReliability (semiconductor)Nonlinear systemPipeline (software)Artificial neural networkLimit (mathematics)Computer scienceEngineeringMathematicsMachine learningMechanical engineeringArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

This paper presents a probabilistic method to assess the lateral buckling response of a pipeline. The method is based on a Monte Carlo (MC) simulation in which the lateral buckling response is predicted through the use of a surrogate model that employs artificial neural networks (ANNs) calibrated from nonlinear finite element (FE) analyses. The method presented intends to improve on current industry best practice by directly considering the limit states relevant to global buckling to produce designs with consistent levels of 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: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.346

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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