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Record W1738954915 · doi:10.1139/cjce-2014-0496

Scenario shakemaps for Montreal

2015· article· en· W1738954915 on OpenAlexafffundvenueabout
Hadi Ghofrani, Gail M. Atkinson, Luc Chouinard, Philippe Rosset, K. F. Tiampo

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsMcGill UniversityWestern University
FundersNatural Resources CanadaStrongCenter for Spintronics Research Network, Tohoku UniversityNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsGround motionInduced seismicitySeismologyMagnitude (astronomy)Event (particle physics)Earthquake scenarioEnvironmental scienceSeismic riskSeismic microzonationPopulationGeographyGeologySeismic hazard

Abstract

fetched live from OpenAlex

Montreal has significant seismic risk due to the combination of moderate seismicity, high population density, and vulnerable infrastructure. An important tool in damage and risk assessment is a scenario shakemap, which shows the expected ground shaking intensity distribution patterns. In this study, we use regional ground motion and site response evaluations to generate scenario shakemaps for Montreal. The impact of event location on expected ground motions and intensities was tested by considering the occurrence of a scenario (a given magnitude event) at various locations, where the scenarios are defined based on an analysis of the most likely future event locations. Variability in near surface geology plays an important role in earthquake ground shaking; we use microzonation information from Montreal to assess the expected site amplification effects. The results of this study may be used as input to seismic risk studies for Montreal.

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 score0.495
Threshold uncertainty score0.959

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 head0.180
Teacher spread0.169 · 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

Citations16
Published2015
Admission routes4
Has abstractyes

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