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Record W2133854405 · doi:10.1071/wf02031

An effective wind speed for models of fire spread

2002· article· en· W2133854405 on OpenAlexfundno aff
Ralph M. Nelson

Bibliographic record

VenueInternational Journal of Wildland Fire · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersMinistry of Natural Resources
KeywordsWind speedMeteorologyEnvironmental scienceWind directionFire regimeAtmospheric sciencesFlame spreadGeologyGeography

Abstract

fetched live from OpenAlex

In previous descriptions of wind-slope interaction and the spread rate of wildland fires it is assumed that the separate effects of wind and slope are independent and additive and that corrections for these effects may be applied to spread rates computed from existing rate of spread models. A different approach is explored in the present paper in which the upslope component of the fire's buoyant velocity is used with the speed and direction of the ambient wind to produce effective values of wind speed and direction that determine the rate of spread vector. Thus the effective wind speed can replace the ambient wind speed in any suitable fire spread model and provide a description of the combined effects on the fire behavior. The difference between current and threshold values of the effective wind speed also can be used to determine whether fire will spread in a given fuel type. The model is tested with data from experiments reported by Weise (1993) in which fire spread was in response to variation in both wind speed and slope angle. The Weise spread rate data were satisfactorily correlated using dimensional methods and the observed spread rate was reasonably well predicted with an existing rate of spread model. Directional aspects of the model were not tested because the Weise (1993) study did not include winds with a cross-slope component.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.243
Teacher spread0.231 · 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

Citations93
Published2002
Admission routes1
Has abstractyes

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