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Record W2103589464 · doi:10.1139/l06-062

Seismic hazard analysis: a comparative study

2006· article· en· W2103589464 on OpenAlexfundvenueaboutno aff
Han Hong, Katsuichiro Goda, A. G. Davenport

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSeismic hazardInduced seismicitySeismologyPolygon (computer graphics)HazardGeologyHazard mapMagnitude (astronomy)Seismic riskEarthquake scenarioVoronoi diagramHazard analysisMathematicsComputer scienceEngineeringGeometry

Abstract

fetched live from OpenAlex

The quantitative seismic hazard maps for the 1970s National Building Code of Canada were evaluated using the Davenport–Milne method. The Cornell–McGuire method is employed to develop recent seismic hazard maps of Canada. These methods incorporate the information on seismicity, magnitude-recurrence relations, and ground motion (or response) attenuation relations. The former preserves and depends completely on details of the historical seismicity; the latter smoothes the irregular spatial occurrence pattern of the historical seismicity into seismic source zones. Further, the Epicentral Cell method, which attempts to incorporate the preserving and smoothing aspect of these methods, has been developed. However, the impact of the adopted assumptions on the estimated quantitative seismic hazard has not been investigated. This study provides a comparative seismic hazard assessment using the above-mentioned methods and simulation-based algorithms. The analysis results show that overall the Davenport–Milne method gives quasi-circular seismic hazard contours near significant historical events, and the Cornell–McGuire method smoothes the transition of contours. The Epicentral Cell method provides estimates approximately within the former and the latter. Key words: epicentral cell method, probability, seismic hazard, Thiessen polygon, Voronoi, uniform hazard spectra.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.199
Teacher spread0.191 · 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 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

Citations38
Published2006
Admission routes3
Has abstractyes

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