Literature review on design fires
Bibliographic record
Abstract
This literature review was carried out to establish the state-of-the-state in the area of design fires and identify future research requirements. The work was necessitated by the need to define simulated fires (design fires) and an experimental set-up for evaluating the fire performance of Canadian houses. The main parameters affecting fire development in small rooms are identified, together with the commonly-employed methods for characterizing design fires for pre-flashover and post-flashover stages of fire development. The majority of methods employed in characterizing post-flashover design fires were found to be based on parametric equations, which attempt to correlate experimental data from various sources, whereas t-squared fires are the most widely used design fires for the pre-flashover stage. Numerous combustion data, from fire tests involving real and mock-up furniture, from various laboratories around the world, was found in the literature. However, it is not possible to collate the data in a neat and organized fashion due to the extremely large variations in furniture designs and materials. Similarly, many fire load surveys have been published over the last two decades and a large variation in fire loads was found, mainly due to geographical differences and the subjective manner in which fire loads are quantified. Most importantly, the literature review revealed an absence of fire load data for residential and commercial occupancies in Canada.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".