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Simulating Campus Evacuation: Case of York University

2016· article· en· W2577854967 on OpenAlexafffund
Ali Asgary, Priscilla Lan Chung Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsUniversity campusPlan (archaeology)WonderPopulationYardTransport engineeringComputer scienceOperations researchEngineeringArchitectural engineeringGeographySociologyPsychologyArchaeology

Abstract

fetched live from OpenAlex

University campuses possess a unique dynamic different than any businesses or grade schools and require a unique perspective when it comes to emergency evacuations. While Universities in North America came across all types of hazards (natural, conflict, and technology) requiring a campus evacuations, the ever-present of these incidences cause one to wonder whether the created plan would ensure sufficient time for the campus community to safely evacuate. As a case study, the York University's Keele campus was selected for its unique surrounding environment (MacMillan Yard, railways lines, fuel storage depot, and Black Creek) and AnyLogic as the program of choice for the agent-based simulation. The model reproduced the number of students (agents) registered during the winter semester of 2014. The weekly cycle runs continuously until informed of an evacuation. The agents then proceeded through four evacuation stages. Overall, the model assisted in visualizing the spatial distribution of agents, understanding peaks in agent populations, the total population in buildings and on campus, and the buildings utilized.

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 score0.500
Threshold uncertainty score0.310

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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