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Record W4230249985 · doi:10.1109/wsc.2018.8632401

MULTI-AGENT BASED SIMULATION OF ELDERLY EGRESS PROCESS AND FALL ACCIDENT IN SENIOR APARTMENT BUILDINGS

2018· article· en· W4230249985 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2018 Winter Simulation Conference (WSC) · 2018
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNetLogoApartmentComputer scienceProcess (computing)Architectural engineeringPopulationAccident (philosophy)Operations researchSimulationRisk analysis (engineering)EngineeringCivil engineeringBusiness

Abstract

fetched live from OpenAlex

A means of egress from buildings is a critical aspect of building design and an important part of building fire regulations. However, elderly evacuees are often overlooked, being regarded as part of the average population, thereby ignoring the limitations elderly people may have. The computational egress model is a useful tool to evaluate postulated "what-if" scenarios, aiming to predict building egress performance under these designated scenarios. This paper first applies Multi-agent Based Simulation (MABS) supported by NetLogo to simulate the evacuation scenarios where evacuees are all elderly people, then statistical analysis is utilized to interpret results, and comparative analysis is conducted to offer some suggestions for egress design and crowd management.

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.

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 score0.329
Threshold uncertainty score0.801

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 head0.031
GPT teacher head0.310
Teacher spread0.279 · 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