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Record W2488783805 · doi:10.5459/bnzsee.33.4.403-420

Understanding torsional phenomena in ductile systems

2000· article· en· W2488783805 on OpenAlexfundno aff
Tom Paulay

Bibliographic record

VenueBulletin of the New Zealand Society for Earthquake Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersUniversidad Nacional de CuyoMcMaster UniversityUniversity of California, San DiegoTechnion-Israel Institute of TechnologyUniversity of Canterbury
KeywordsIdentification (biology)Set (abstract data type)Code (set theory)Computer scienceConstruction engineeringEngineeringProgramming language

Abstract

fetched live from OpenAlex

Recent studies provided opportunities to review some of the principles, which have been used in the formulations of internationally accepted code-recommendations relevant to the seismic design of ductile buildings also subjected to torsional phenomena. With the progress of this study, features emerged which are considered to have contributed to a better understanding of structural behaviour. Moreover, the identification of deeply embedded fallacies, relevant to ductile response, suggested the introduction of some changes in seismic design strategies, yet not widely known or appreciated. Reasons for necessary re-interpretations of traditional structural properties, together with illustrative examples, demonstrating applications, rather than set code-type rules, are offered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.182
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations17
Published2000
Admission routes1
Has abstractyes

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