Effects of Different Suppression Tactics on the Firefighter and Compartment Environment
Bibliographic record
Abstract
Full-scale experiments are conducted to study the effects of different water-based indirect initial attack methods on the compartment environment and firefighter during compartment fire suppression. Hot layer temperatures typical of room fire conditions are developed in the test compartment using wood cribs. Five suppression methods including straight stream, penciling, continuous wide and narrow fog, and a wide angle burst method are examined for two different spray angles and nozzle pressures. Temperatures, heat flux, gas velocity, and gas concentrations are monitored for the duration of the experiment in the fire compartment. Realistic, yet extreme, fire conditions are repeatedly established in the test compartment, with the fuel load allowing up to nine tests per fire. Differences in average compartment temperature before and during suppression indicate that penciling tactics provide little cooling of the compartment. In narrow fog attacks, the hot layer is pushed toward the floor, resulting in increased temperatures in the lower layer, generally an undesired result. Wide angle fog methods have greater impact on compartment temperature as compared to straight stream or narrow fog methods; however, they may also result in large increases in temperature at the firefighter. Wide angle burst tactics less effectively cool the compartment gases than continuous methods, but also lead to less impact on the firefighter. Greater numbers of bursts increase cooling of the compartment, but at the expense of increased impact on the firefighter. Including impact on the firefighter, continuous straight stream methods, at a nozzle pressure of 700 kPa and aimed to the top of the rear compartment wall, appear the best choice for initial attack on fires developed in these experiments. Due to variability between real fire scenarios and experiments such as these, significantly more study of the various suppression tactics is required before the most effective methods of suppression can be determined for a given set of fire scenarios.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".