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Record W2114858213 · doi:10.1002/eqe.2513

Record selection for aftershock incremental dynamic analysis

2014· article· en· W2114858213 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEarthquake Engineering & Structural Dynamics · 2014
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersUniversity of British ColumbiaAlexander von Humboldt-Stiftung
KeywordsAftershockFragilityGeologySeismologySelection (genetic algorithm)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Summary The back‐to‐back application of mainshock records as aftershock is often considered in conducting aftershock incremental dynamic analysis. In such an approach, the characteristics of mainshock records are considered to be similar to those of major aftershock records within the same mainshock–aftershock sequences. The underlying assumption is that the characteristics of selected mainshocks, other than those used for record selection, are not significant in the assessment of structural responses. A case study is set up to investigate the effects of aftershock record selection on the collapse vulnerability assessment. The numerical results for a specific wood‐frame structure indicate that the aftershock fragility can be affected by the aftershock record characteristics, particularly response spectral shape. Copyright © 2014 John Wiley & Sons, Ltd.

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 categoriesMeta-epidemiology (narrow)
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 score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.190
Teacher spread0.187 · 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