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Record W1964696931 · doi:10.1071/wf02006

An examination of the sensitivity of numerically simulated wildfires to low-level atmospheric stability and moisture, and the consequences for the Haines Index

2002· article· en· W1964696931 on OpenAlexaff
Mary Ann Jenkins

Bibliographic record

VenueInternational Journal of Wildland Fire · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsYork University
FundersNational Center for Atmospheric Research
KeywordsEnvironmental scienceDrynessMoistureAtmospheric instabilityAtmosphere (unit)MeteorologyAtmospheric sciencesStability (learning theory)Fire regimeWind speedClimatologyGeographyGeologyComputer scienceEcosystem

Abstract

fetched live from OpenAlex

The Haines Index, an operational fire–weather index introduced in 1988 and based on the observed stability and moisture content of the near-surface atmosphere, has been a useful indicator of the potential for high-risk fires in low wind conditions and flat terrain. The Haines Index is of limited use, however, as a predictor of actual fire behavior. To develop a fire–weather index to predict severe or erratic wildfire behavior, an understanding of how the ambient lower-level atmospheric stability and moisture affects the growth of a wildfire is needed. This study is a first step in this process. This study investigates, through four comparative numerical simulations with a coupled wildfire–atmosphere model, the sensitivity of wildland fires to atmospheric stability and moisture, and in the process explores the correspondence between atmospheric stability and moisture, wildfire behavior, and the Haines Index. In the first three fire simulations, the model atmosphere was initially set to identical moisture but different instability conditions that correspond to Haines Indexes for low, moderate, and high potential for severe fire development. In the fourth fire simulation, the initial atmospheric and moisture conditions were for a high-risk fire Haines Index rating, but different from the initial conditions of dryness and stability of the previous experiments. The study indicates that high-risk fire development is sensitive to near-surface atmospheric stability and moisture, and that there is a range of atmospheric stability and moisture conditions that is important to the development of severe or erratic fire behavior, and that this range is within the atmospheric stability and moisture conditions represented by a Haines Index for high potential for severe fire. The analyses also suggest that there is a substantial latitude of fire behavior for fires rated as this Index, indicating that this Index should be further divided, or refined.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.228
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 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

Citations18
Published2002
Admission routes1
Has abstractyes

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