Local application water mist system for fire suppression
Bibliographic record
Abstract
The development of water mist fire suppression technology has made substantial progress over the last decade. It has been used to replace techniques no longer deemed environmentally acceptable, such as halons, or to provide answers to problems where traditional technologies have not been effective [1-6]. Water mist systems, according to their applications, have been distinguished into three types: total compartment application (TCA), local application (LA) and zoned application (ZA) systems [7]. For a TCA system, water is discharged from all nozzles distributed throughout the compartment. A large and enclosed fire can be extinguished by a TCA system as the oxygen concentration in the compartment is quickly dropped due to the consumption by the fire and the displacement by water vapour. However, it is difficult for a TCA system to extinguish small or hidden fires [8], and water damage may result, as a large quantity of water is discharged. An LA water mist system, like a zoned application system, extinguishes a fire mainly by cooling flames or by cooling the fuel surface. It is arranged to discharge directly to an object or hazard in an enclosed, partially enclosed, or open outdoor area. It extinguishes small and hidden fires more effectively than a TCA system. LA water mist systems have been used to provide the protection for engine test cells, bulk conveyors, flammable liquid storage racks, commercial cooking areas and industrial oil cookers [9-12].
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".