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Record W6929409227 · doi:10.4224/23002018

Numerical modelling of water mist systems in protection of mass timber residential buildings

2020· report· en· W6929409227 on OpenAlexaffvenue

Bibliographic record

VenueNPARC · 2020
Typereport
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMistCompartment (ship)Fire protectionFire Dynamics SimulatorParametric statisticsFirefighting

Abstract

fetched live from OpenAlex

This report presents the findings from a simulation parametric study to investigate the use of water mist systems for a residential compartment fire involving exposed mass timber structures. The fire and suppression models were first validated against experimental data obtained from the NRC fire tests that were conducted under the same project. Seventeen simulations were conducted using Fire Dynamic Simulator (FDS) software. The following parameters were investigated; 1- Effect of fuel arrangement and location on fire severity in exposed wood compartment 2- Effect of different finishing on fire severity in compartment 3- Fire and suppression in open space vs compartment 4- Effectiveness of water mist systems in fire suppression in compartments with different finishing. The results show the effectiveness of the water mist system in suppressing the fire in exposed wood compartments where a high heat release is expected due to the high fuel load.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.122
GPT teacher head0.370
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes2
Has abstractyes

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