Performance‐Based Approach for Classifying the Degree of Combustibility of Building Products
Bibliographic record
Abstract
ABSTRACT Managing fire behaviour of building products is crucial to provide fire safety. In North America, the classification of building products according to their fire risk is based on a binary system, with products designated as either noncombustible or combustible through a vertical tube furnace test. For products that are classified as combustible, legislators require a comparative flame spread rating based on the Steiner tunnel test to differentiate the fire risks associated with the products' combustibility. However, these standardized test methods do not indicate the fire properties and dynamics of the building products, such as the heat release rate. This paper presents an alternative approach to classifying building products based on fire‐dynamic quantities from cone calorimeter tests. The fire risk model results were compared with the Steiner tunnel classifications and predictive approaches for room corner tests and European Euro class. The fire risk model facilitates the classification of building products according to their degree of combustibility based on engineering variables related to fire dynamics. In addition, the model results offer a reasonable indication of fire performance at intermediate and large scales.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".