MétaCan
Menu
Back to cohort
Record W2981959566 · doi:10.4095/226355

Deaggregation of seismic hazard for selected Canadian cities

2004· report· en· W2981959566 on OpenAlexaffabout
S Halchuk, J Adams

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSeismic hazardHazardEnvironmental scienceSeismologyGeologyGeographyBiologyEcology

Abstract

fetched live from OpenAlex

The Geological Survey of Canada's new seismic hazard model for Canada will form the basis for the seismic design provisions of the 2005 National Building Code of Canada (NBCC). We deaggregate the seismic hazard results for selected cities to help understand the relative contributions of the earthquake sources in terms of distance and magnitude. Deaggregation for a range of spectral accelerations (Sa(0.1) to Sa(2.0) seconds) and for a number of probabilities (2%/50, 5%/50, 10%/50, 20%/50, 40%/50 years) is performed to examine in detail the hazard for two of Canada's largest urban centres at high risk, Vancouver in the west and Montreal in the east. Additional plots and a summary table of deaggregated seismic hazard are provided for other selected Canadian cities, for Sa(0.2) and Sa(1.0) seconds and for a probability of exceedence of 2%/50 years. In most cases, as the probability decreases, the hazard sources closer to the site dominate. Larger, more distant earthquakes contribute more significantly to hazard for longer periods than shorter periods. Deaggregation plots can provide useful information on the distance and magnitude of predominant sources, which can be used to generate scenario earthquakes and select corresponding time histories for seismic design.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.081
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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

Citations29
Published2004
Admission routes2
Has abstractyes

Explore more

Same topicSeismic Performance and AnalysisFrench-language works237,207