Meta-analysis of compartment fires: Exploring extensive experimental datasets with heat release rate in focus
Bibliographic record
Abstract
This study reviews and analyzes 112 compartment fire tests to provide insights into fire behavior in realistic scenarios. The complex nature of compartment fire dynamics is emphasized by the significant variability in the collected data, which currently poses challenges for the development of engineering tools based on physical models. The results indicate that fuel load density alone does not fully account for fire hazard due to the impact of other factors on the maximum heat release rate (HRR) while higher compartment shape factor, defined as the ratio of total area (A T ) to floor area (A F ), result in reduced HRR due to greater heat loss and ventilation limitations. In the fire’s growth phase, effective removal of hot gases through openings can slow fire growth by reducing thermal feedback. In addition, increased fuel load density and furniture fuels, containing high calorific materials, shortens the time required to reach maximum HRR and prolongs post-flashover duration; reduced opening factors delay peak HRR time and extend post-flashover durations. It can be concluded that effective fire safety design necessitates considering the interconnection of all parameters for accurate predictive modeling.
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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 | 0.043 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".