A taxonomy of concurrent upward flame spread models and sources of uncertainty
Bibliographic record
Abstract
The use of flame spread models as a tool in fire safety engineering practice is subject to uncertainties arising from the assumptions inherent in the models and the input parameters required to implement them. Research on flame spread continues to add complexity to existing models, which places a greater onus on suitable input parameters to provide the knowledge needed to implement them. This approach is appropriate when experimental datasets are available for the validation of models for research purposes. However, it can be detrimental for design applications when there is limited knowledge about the underlying parameters for model verification in a broad range of design applications. Therefore, increasing complexity in flame spread models has the potential to reduce the strength of any such decisions made. In this article we provide a comprehensive review of the complexity of different flame spread models. This review classifies model approaches based on their inherent assumptions and sub-models to describe the complexity of the problem. It shows that, while complex modelling approaches may be necessary to adequately represent complex phenomena such as flame spread, the impact of uncertainties associated with the additional inputs is not always quantified or justified.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".