Bibliographic record
Abstract
This thesis covers a variety of analytical approaches that validate the use of the EvacuatioNZ model on high-rise building analysis. Through performing a number of sensitivity analyses, several model deficiencies as well as functional limitations were improved upon and part of the model developments are continued based on the previous research done by two Master's students at the University of Canterbury. In this thesis, data from three evacuations were considered for different validating aspects. These evacuations were, a hypothetical 21-storey hotel building located in the United States of America, which was previously simulated using Simulex and EXIT89; a trial evacuation that was carried out in a 13-storey office building located in Canada; and a fire drill conducted at a 21-storey office building located in Australia. Overall, the results indicated that the EvacuatioNZ is able to produce reasonable predictions of the total evacuation time regardless of the number of floors involved. The component testing also showed satisfactory outcomes regarding the involvement of disabled occupants, complexity of node configurations, and different pre-movement time distributions. However, the current model still has a number of limitations that need to be verified and tested. These include the preferred route function and the connection problem for long stairs. Further research should also be carried out on the use of the Evacuation model on other types of building structures so as to increase the confidence level of utilizing the EvacuatioNZ model for general applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".