The Comparison of CAF with air aspirated and unexpanded foam water solutions
Bibliographic record
Abstract
Air aspirated foam is created close to or in the nozzle by entraining air with a water jet and impacting on one or more obstacles. Some of the energy of the stream is used to agitate the mixture and foam is produced. 'Foam-water' refers to a foam solution of water and foam concentrate that has not been expanded by air. Compressed-air foam (CAF) has been proven to be an effective fire suppression material for both Class A and B fires, however, the effectiveness of CAF compared to air-aspirated systems and foam water systems has not yet been quantified. While air-aspirated foam systems have been around for many years the development of high quality Aqueous Film-Forming Foam (AFFF) concentrates have allowed foam systems with little or no air expansion to be used in controlling large flammable liquid fuel fires. To effectively compare these systems a series of 22 full scale fire tests were conducted with CAF, air-aspirated foam and foam water solution using 4.64 m2 pool fire. This paper describes a series of 22 full scale Class B fire tests designed to compare CAF, air-aspirated, and foam water solution in extinguishing a 4.64 m2 pool fire in accordance with the CAN/ULC-S560-98 Standard for Category 3 Aqueous Film-Forming Foam (AFFF) Liquid Concentrates [1]. In addition to visual observations, radiant heat flux was also measured at a point 1.83 m from the edge of the fuel pan and 1.5 m off the ground.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".