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Record W2981423291 · doi:10.4095/296256

A profile of earthquake risk for the District of North Vancouver, British Columbia

2015· report· en· W2981423291 on OpenAlexaffabout
J M Journeay, F Dercole, D Mason, M Westin, Jorge A. Prieto, C L Wagner, N L Hastings, Sung-Ja Chang, A Lotze, Carlos E. Ventura

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsResilience (materials science)Environmental planningNatural disasterEnvironmental resource managementGovernment (linguistics)Disaster risk reductionRisk managementEmergency managementRisk assessmentGeographyBusinessNatural hazardCommunity resilienceEconomic growthEnvironmental scienceFinanceComputer scienceComputer securityResource (disambiguation)Economics

Abstract

fetched live from OpenAlex

The societal costs of natural hazards are large and steadily increasing in Canada due to increased urban development, an aging infrastructure, and limited capacities to anticipate and plan for unexpected disasters. Lessons learned from recent disasters underscore the need for a comprehensive risk-based approach to land use planning and emergency management at all levels of government-one that utilizes available knowledge about the risk environment to inform actions that have a potential to minimize future disaster losses and increase the resilience of communities to the dynamic and uncertain forces of change. We cannot predict or prevent earthquakes from happening. However, we do have the knowledge and capabilities to change the outcome of earthquake disasters through a combination of risk assessment and disaster resilience planning. Risk assessment is the process through which knowledge about a community and its exposure to natural hazards is used to anticipate the likely impacts and consequences of an unexpected event at some point in the future. Disaster resilience planning is focused on actions that can be taken in advance to balance policy trade offs for growth and development (opportunities) with risk reduction investments that have a potential to minimize future losses (liabilities) while increasing capabilities of a community to withstand, respond to and recover from unexpected disaster events (resilience). This study provides a detailed assessment of earthquake risk for the District of North Vancouver - an urban municipality of approximately 83,000 people situated along the North Shore Mountains in southwestern British Columbia. It describes the probable impacts of a significant earthquake with greater clarity and detail than ever before, and develops both a methodology and target criteria to guide future risk reduction and disaster resilience planning activities through the lens of building performance, public safety, lifeline resilience and socioeconomic security. We examine cause-effect relationships and seismic risks for a plausible earthquake scenario in the Strait of Georgia (M7.3), and undertake a more general assessment of who and what are vulnerable to known earthquake hazards in the region using probabilistic ground motion models that are consistent with those used to establish seismic safety guidelines in the National Building Code of Canada (NBCC, 2010). Study outputs offer a capacity to explore thresholds of risk tolerance and opportunities for mitigation through ongoing emergency planning and land use decision-making activities in the community. Methodologies and insights gained through this study are transferrable to other communities who may face similar challenges of managing growth and development in areas exposed to earthquake hazards. Key findings and recommendations of the study contribute to broader efforts led by the Canadian Safety and Security Program to support disaster resilience planning at a community level in Canada.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.540
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2015
Admission routes2
Has abstractyes

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