COMPARISON OF MODEL PREDICTIONS AND ACTUAL EXPERIENCE OF OCCUPANT RESPONSE AND EVACUATION IN TWO HIGHRISE APARTMENT BUILDING FIRES
Bibliographic record
Abstract
The predictions of the National Research Council of Canada's risk-cost assessment model FiRECAM are compared with the actual experience of occupant response and evacuation in two highrise apartment building fires. The first fire is a 29-storey building fire that resulted in 6 casualties in the stairshafts and the second one is a 25-storey building fire that had a casualty in the apartment of fire origin and a subsequent death a few days later due to a heart attack. FiRECAM employs a fire growth sub-model to predict how the fire develops in the apartment of fire origin, a smoke movement sub-model to predict how the smoke spreads in the building, including the time when the stairs become untenable and cannot be used by the occupants to evacuate. FiRECAM also employs occupant response and evacuation sub-models to predict the behaviour of the occupants. Occupant behaviour in a fire evacuation is difficult to model. The comparisons show that the model predictions are in reasonable agreement with the actual evacuation experiences of these 2 building fires.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".