A Pilot Study to Assess the Feasibility of Comparing Ultra-High Pressure to Low-Pressure Fire Suppression Systems for a Simulated Indirect Exterior Attack
Bibliographic record
Abstract
Financial and human resource challenges constrain firefighting in rural communities. This can limit the approaches that can be used in a given residential fire situation. Effective use of portable, lower-cost equipment that would require fewer personnel and less water could greatly benefit rural communities. This study was conducted to assess the feasibility of comparing ultra-high-pressure to low-pressure fire suppression systems at low flow rates. The conditions used simulated an indirect exterior attack through a window. A purpose-built burn room and standardized class A fires were used to compare ultra-high-pressure and low-pressure systems at low flow rates. Temperatures in the burn room were recorded for each condition in triplicate. While neither operating condition resulted in full extinguishment of the fire, the ultra-high-pressure trials saw decreases in the proportion of starting temperature that were faster and of greater magnitude than for the low-pressure trials. This compares with earlier research, simulating a transitional attack that saw similar patterns for temperature cooling but resulted in extinguishment. This preliminary testing provides evidence that the burn container and room, as well as instrumentation and fuel load configurations, are appropriate for more extensive testing of such equipment for exterior fire suppression.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".