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Record W2050506857 · doi:10.1115/pvp2008-61013

Some Topics in the Modal FRS Method

2008· article· en· W2050506857 on OpenAlexaff
B. L. Ly, Lijuan Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsModalAccelerationModal analysisMode (computer interface)ResidualComputer scienceNormal modeFinite element methodStructural engineeringAlgorithmAcousticsPhysicsEngineeringVibrationClassical mechanicsMaterials science

Abstract

fetched live from OpenAlex

The following five common topics in the modal Floor Response Spectrum (FRS) method are examined: 1) The relationship between the effective mass and the total mass; 2) Estimation of the base shear; 3) Identification of a local mode; 4) The uniform acceleration to be applied in the equivalent static analysis method; 5) Correction for the residual rigid modes. This paper shows the usage of effective mass in determining the base shear. A quantitative method based on the ranking of the importance of a mode and the importance of a mass in a mode is given, in addition to graphic animations, to exclude a local mode. It also provides a more realistic static method with a less overly conservative acceleration.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0030.005
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.006

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 head0.042
GPT teacher head0.334
Teacher spread0.292 · 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 designTheoretical or conceptual
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

Citations0
Published2008
Admission routes1
Has abstractyes

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