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Record W3214620769 · doi:10.1007/s10518-021-01258-y

Hazard-consistent seismic losses and collapse capacities for light-frame wood buildings in California and Cascadia

2021· article· en· W3214620769 on OpenAlexaff
Robert E. Chase, Abbie B. Liel, Nicolas Luco, Zach Bullock

Bibliographic record

VenueBulletin of Earthquake Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation of Sri LankaNational Science Foundation
KeywordsSubductionSeismic hazardSeismologyGround motionGeologyHydrogeologyHazardFrame (networking)Geotechnical engineeringEngineeringTectonics

Abstract

fetched live from OpenAlex

Abstract We evaluate the seismic performance of modern seismically designed wood light-frame (WLF) buildings, considering regional seismic hazard characteristics that influence ground motion duration and frequency content and, thus, seismic risk. Results show that WLF building response correlates strongly with ground motion spectral shape but weakly with duration. Due to the flatter spectral shape of ground motions from subduction events, WLF buildings at sites affected by these earthquakes may experience double the economic losses for a given intensity of shaking, and collapse capacities may be reduced by up to 50%, compared to those at sites affected by crustal earthquakes. These differences could motivate significant increases in design values at sites affected by subduction earthquakes to achieve the uniform risk targets of the American Society of Civil Engineers (ASCE) 7 standard.

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: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.179
Teacher spread0.173 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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