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Record W2981426491 · doi:10.4095/215330

Effect of earthquake probability level on loss estimations

2004· report· en· W2981426491 on OpenAlexaboutno aff
T. Onur, Carlos E. Ventura, W. D. Liam Finn

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsStatisticsSeismologyGeologyEnvironmental scienceGeodesyMathematics

Abstract

fetched live from OpenAlex

The ground shaking intensity used for calculating the quasi-static forces to be applied in building design according to the National Building Code of Canada (NBCC) is established by probabilistic seismic hazard analyses. The probability level for which the amplitudes of design motions are determined in the current building code, NBCC-1995, corresponds to a 10% chance of exceedance in 50 years. The insurance industry has funded loss estimation studies at the University of British Columbia for a number of cities in British Columbia, including the largest, Vancouver. This city has the highest seismic hazard among the three most populated urban centres in Canada. A major objective of the studies was to provide a rational basis for discussions with government on how to cope with catastrophic losses in the region. For this reason, it was considered appropriate to use the same probability of exceedance of shaking intensity as that used in NBCC-1995. The seismic provisions of the next edition of the code, NBCC-2005, will be based on a probability level corresponding to a 2% chance of exceedance in 50 years. This paper investigates the impact of this change on the loss estimations for Vancouver. The concepts and strategies used in the Vancouver study are of wide applicability and should be of interest to others engaged in risk assessment.

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)0.023
metaresearch head score (Gemma)0.163
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.163
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.292
Teacher spread0.232 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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