Collection and Use of Data from School Egress Trials
Bibliographic record
Abstract
Data was collected from five evacuations from the same school building, conducted in Spain between 2011 and 2014. Children from 6 to 16 years old were observed during the evacuation exercises. Four of the evacuations were unannounced, while one was semi-announced: the staff being aware that the drill would be conducted on a particular day, while the students were unaware. Information was gathered on the key factors deemed to influence evacuation performance: a description of the geometry, the population involved, the procedures employed and the organization of the drill itself. This information should allow interested parties to gain a reasonably detailed understanding of the initial conditions of each of the five trials. Evacuation data was also collected, focusing on the pre-evacuation times, the routes employed, the speeds adopted and the arrival times. Where data is ambiguous, flawed or omitted, this is documented in an attempt at transparency. To demonstrate an application of this data, we performed a series of small test cases using the Pathfinder, STEPS and EXODUS evacuation tools. 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) make public the configuration files (e.g. architectural files, raw experimental data, etc.) and analysis to contribute to the understanding the emergency movement of school pupils and the subsequent use of this data as part of modelling validation exercise. It is suggested that the very transparency of this process is relatively novel in the area of egress modelling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".