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Record W1993250849 · doi:10.1193/030713eqs065m

A Consistent Cross‐Border Seismic Hazard Methodology for Loss Estimation and Risk Management along the Border Regions of Canada and the United States

2013· article· en· W1993250849 on OpenAlexaboutno aff
P.C. Thenhaus, Kenneth W. Campbell, Nitin Gupta, David F. Smith, Mahmoud M. Khater

Bibliographic record

VenueEarthquake Spectra · 2013
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersU.S. Geological Survey
KeywordsSeismic hazardInduced seismicitySeismologySubductionGeologyEarthquake scenarioSeismic riskGround motionHazardMetropolitan areaHazard mapGeographyArchaeologyTectonics

Abstract

fetched live from OpenAlex

We provide a methodology that seamlessly integrates national seismic hazard models across the Canada‐U.S. border to provide earthquake risk managers with updated and consistent seismic hazard science and technology in the two countries. Consistent with our U.S. hazard model, we developed a new Canadian model that incorporates (1) spatially varying seismicity for the major metropolitan areas of southeastern and southwestern Canada and the United States, (2) a comprehensive probabilistic model for the Cascadia subduction zone that includes M 8.0–9.2 interface earthquakes, (3) a consistent set of ground motion prediction equations across eastern and western North America, and (4) a soil‐based attenuation (SBA) methodology that mitigates uncertainty in the conversion of earthquake motions from rock to soil, on which the majority of exposure is located. NEHRP site conditions are mapped for all of Canada from existing geological data, and NEHRP site factors are used to account for local site conditions.

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 score0.073
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.263
Teacher spread0.251 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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