School egress data: comparing the configuration and validation of five egress modelling tools
Bibliographic record
Abstract
Summary Data were collected between 2011 and 2014 from five evacuations involving the same school buildings located in Spain. Children from 6 to 16 years of age were observed during the evacuation exercises. Background information was collected on key factors deemed to influence evacuation performance: a description of the geometry, the population involved, the procedures employed and the organization of the drills conducted. Using live observations and video footage of these drills, evacuation data were collected, focusing on the pre‐evacuation times, the routes employed, the travel speeds adopted and the arrival times. These data informed a range of a posteriori simulations, conducted by using four computer models (buildingEXODUS, MassMotion, Pathfinder and STEPS) and the Society of Fire Protection Engineering hydraulic model (i.e. Society of Fire Protection Engineering hand calculations). Comparisons were drawn between the models' output and against the observed outcome for one of the trials to determine the accuracy of the model predictions given that they were configured by using the initial conditions for a specific evacuation. The purpose of this work is to (1) provide insight into the configuration of these models for equivalent scenarios, (2) examine any variation in the simulated conditions given equivalent initial conditions, and (3) provide suggestions on how to perform validation studies for multiple evacuation models. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".