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Record W1913339550 · doi:10.1115/1.1526858

Limit Load Bounds for the mα Multiplier

2003· article· en· W1913339550 on OpenAlexaff
Wolf Reinhardt, R. Seshadri

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLimit (mathematics)Limit loadBounding overwatchLimit state designStress (linguistics)MathematicsStatistical physicsStructural engineeringComputer scienceMathematical analysisPhysicsEngineeringFinite element method

Abstract

fetched live from OpenAlex

Limit analysis is an important tool to assess the integrity of a structure. Successive elastic modulus adjustment, in particular, can simulate the plastic stress redistribution in a structure at the limit state (plastic collapse). Several limit load estimates have been developed based on the stress and strain distribution within a component. The bounding nature of these estimates is examined in this paper. Particular emphasis is given to the limit load estimate mα, which has been proposed to give improved limit load predictions from partly converged stress distributions in the structure. Bounds on the accuracy of these mα predictions are derived.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.348

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 head0.010
GPT teacher head0.221
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

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 designNot applicable
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

Citations23
Published2003
Admission routes1
Has abstractyes

Explore more

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