MétaCan
Menu
Back to cohort
Record W7139526044

The evolution of gasses in fire suppression system to promotechanges in industrial towards global sustainability

2021· article· en· W7139526044 on OpenAlexaboutno aff
Jum’azulhisham Abdul Shukor, Rahim Jamian, Azmi Hassan, Muhammad Al’Hapis Abdul Razak

Bibliographic record

VenueUTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia) · 2021
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityGreenhouse gasGlobal warmingSustainable developmentHazardous wasteOzone layerFire protectionSupply chain
DOInot available

Abstract

fetched live from OpenAlex

The historical development of fire suppression technology evolved in the 1930s since the application of Halons as a fire extinguishing agent. The fire may cause tremendous losses to organizations. It affects the chain of businesses and the stability of the economic growth of a country. The key issues of greenhouse effects and safety and health as well contributes to the sudden change of the technology of fire extinguishing systems. The establishment of the Montreal Protocol and Kyoto Protocols controls the producers to develop, supply and use of environmentally hazardous gasses worldwide. Hence, promote global sustainable for upcoming generations. This paper is highlighting the reasons gas type fire extinguishing agents extensively used substituting conventional methods against fire. The fundamental equations of Ozone Depleting Potential and Global Warming Potential were properly discussed to show how severe these gasses exposed to the environment. The effectiveness of these gases as a clean agent in extinguishing the fire may convince prospect users to carry out the decision of changes. Potential extinguishing agents will be deliberated to investigate their needs as new fire suppression agents. It will be then to be suggested and recommended for further studies.

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 score0.091
Threshold uncertainty score0.999

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueUTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia)Same topicFire dynamics and safety researchFrench-language works237,207