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Record W2038628266 · doi:10.1080/00102200302347

A physically based model of the onset of crowning

2003· article· en· W2038628266 on OpenAlexaboutno aff
B. Porterie, J. C. Loraud, Laurent Bellemare, Jean-Louis Consalvi

Bibliographic record

VenueCombustion Science and Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsTurbulenceIgnition systemMechanicsEnvironmental scienceMeteorologyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

The objective of this study is to investigate the capability of a physical two-phase model to predict the ignition of crown fuels by a surface fire and then to determine the degree of crowning. The model considers the hydrodynamic aspects of the flow and accounts for the basic physicochemical processes resulting from the thermal degradation of organic matter. Turbulence, soot formation, and its impact on radiation are considered in order to improve the physical insight. Calculations have been performed to investigate the effects of crown base height and aerial fuel moisture content on the onset of crowning. Numerical results are found to be consistent with experimental observations and the widely used Canadian Fire Behavior Prediction System classification by crown fraction burned. This model may be used to extend the domain of application of semiphysical theories, e.g., Van Wagner's theory, where fuel and environmental factors are generally determined from empirical observations of previous fires. It provides a means of adjusting these factors in other fire situations without requiring additional experiments.

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: none
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.233
Teacher spread0.221 · 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

Citations17
Published2003
Admission routes1
Has abstractyes

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