Class B Fire-Extinguishing Performance Evaluation of a Compressed Air Foam System at Different Air-to-Aqueous Foam Solution Mixing Ratios
Bibliographic record
Abstract
The purpose of this research is to evaluate the fire-extinguishing performance of a compressed air foam system at different mixing ratios of pressurized air. In this system, compressed air is injected into an aqueous solution of foam and then discharged. The experimental device uses an exclusive fire-extinguishing technology with compressed air foam that is produced based on the Canada National Laboratory and UL (Underwriters Laboratories) 162 standards, with a 20-unit oil fire model (Class B) applied as the fire extinguisher. Compressed air is injected through the air mixture, and results with different air-to-aqueous solution foam ratios of 1:4, 1:7, and 1:10 are studied. In addition, comparison experiments between synthetic surfactant foam and a foam type which forms an aqueous film are carried out at an air-to-aqueous solution foam ratio of 1:4. From the experimental results, at identical discharging flows, it was found that the fire-extinguishing effect of the aqueous film-forming foam is greatest at an air-to-aqueous solution foam ratio of 1:7 and weakest at 1:10. Moreover, the fire-extinguishing effect of the aqueous film-forming foam in the comparison experiments between the aqueous film-forming foam and the synthetic surfactant foam is greatest.
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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 | 0.000 |
| 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".