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Framework for Earthquake Evacuation Planning: Case Study for Montreal, Canada

2012· article· en· W2062486141 on OpenAlexaffabout
Umma Tamima, Luc Chouinard

Bibliographic record

VenueLeadership and Management in Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsDisaster planningEarthquake scenarioBusinessForensic engineeringEngineeringEnvironmental planningCivil engineeringConstruction engineeringGeographySeismic hazardPoison controlHuman factors and ergonomicsMedical emergency

Abstract

fetched live from OpenAlex

Disaster management planning in Canada is government led, and the responsibilities are divided among federal, provincial, and local levels of government. The Civil Emergency Plan of Greater Montreal (Plan de sécurité civile de l’agglomération de Montréal) describes operations and responsibilities during emergencies and provides a framework for the strategic management cycle, which includes action, coordination, and communication between various decision centers. However, evacuation planning processes have not been described for any of these levels of government. Recognizing the necessity for evacuation planning, this paper proposes a three-step framework for earthquake evacuation planning for Montreal: (1) vulnerability assessment of the built environment and community demographics, (2) shelter and evacuation route planning, and (3) preparation of community evacuation maps. This paper also acknowledges the need for participatory and advocacy planning during the preparation of community evacuation plans.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.420

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.057
GPT teacher head0.275
Teacher spread0.218 · 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 designObservational
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

Citations13
Published2012
Admission routes2
Has abstractyes

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