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Record W4386424657 · doi:10.7731/kifse.95785771

Experiments on the Application of Class A and B Fires to Derive the Optimum Air Ratio of Compressed Air Foam Systems

2023· article· en· W4386424657 on OpenAlexaff
Tae-Hee Park, Tae‐Sun Kim, Jeong-Hwa Park, Ji-Hyun Yang, Byeong-Chae Lee, T.K. Kim, Bong-Jun Kim, Jin-Suk Kwon

Bibliographic record

VenueFire Science and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsNational Research Council Canada
FundersNational Fire Agency
KeywordsCompressed airClass (philosophy)Drop (telecommunication)Equivalence ratioAir temperatureEnvironmental scienceMaterials scienceMeteorologyEngineeringComputer scienceMechanical engineeringChemistryCombustionArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

This experiment was designed to increase the utilization of fire engines equipped with compressed air foam systems (CAFS) at the scene of a fire. The purpose of this experiment was to determine the optimum air ratio for the CAFS. Wood crib fires (class A) and steel pan fires (class B) were both extinguished using synthetic surfactant CAFS installed in a fire engine. According to the temperature drop rate (°C/s), the optimum air ratio was thirteen and five in the class A and B fires, respectively. The results derived from this experiment can be used to strengthen fire scene response capabilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.248
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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