The fire hazard potential of thermally thin cellulose nonwovens with different overlap configurations
Bibliographic record
Abstract
The fire hazard potential of nonwovens with different overlap configurations and various thicknesses between 0.5 and 5 mm were investigated to clarify the possible worsening situation induced by the difference in overlapping configurations. The fire hazard of thermally thin nonwovens is characterized by flame spread, flame height, temperature distribution, and mass loss. The flame spread rate and FDI (fire development index) increase and then decrease as the thickness of the nonwovens increases. The fire hazard potential of samples with OL configuration (overlapping layer by layer configuration) is higher than that with AO configuration (accordion-style overlap configuration), which is reflected in the high FDI, average fire spread rate, and flame height, and low ignition time. The difference between the fire risk of the two overlapping configurations was confirmed by analyzing the heat transfer mechanism. Due to the different packaging methods, the AO and OL configurations of nonwovens show different degrees of shrinkage and curling effects during combustion. The large air gap between the layers and the weight of the sample edge are the reasons for the weak contraction of the sample with an AO configuration. The sample shrinkage with OL configuration strengthens the flame spread to a certain extent by increasing the convective heat transfer in the preheat zone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".