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Record W1984392181 · doi:10.1139/x06-158

A heat transfer model of crown scorch in forest fires

2006· article· en· W1984392181 on OpenAlexfundvenueno aff
Sean T. Michaletz, Edward A. Johnson

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Conservation Association
KeywordsCrown (dentistry)PlumeHeat transferEnvironmental scienceAtmospheric sciencesMeteorologyMechanicsMaterials scienceGeologyPhysicsComposite material

Abstract

fetched live from OpenAlex

The Van Wagner crown scorch model is widely used to estimate crown component necroses in surface fires. The model is based on buoyant plume theory but accounts for crown heat transfer processes using an empirical proportionality factor k. Crown scorch estimates have used k values for foliage, but k varies with heat transfer characteristics, and branch and bud necroses are more relevant to tree mortality. This paper derives and validates a more physically complete model of crown scorch in surface fires (I ≤ 2500 kW·m –1 ). The model links a buoyant plume model with a lumped capacitance heat transfer analysis applicable to branches, buds, and foliage (~1 cm maximum diameter). The lumped capacitance analysis is validated with vegetative-bud heating experiments, and the entire heat transfer model of crown scorch is validated with fireline intensity and foliage necrosis data. The model is more general than the Van Wagner model and is independent of experimental fire data. Predictions require measurements of fireline intensity, residence time, ambient temperature, and five thermophysical properties of crown components. The model predicts differences between bud and foliage necrosis heights, and illustrates why heat transfer processes should be considered in crown scorch models.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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 designObservational
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

Citations82
Published2006
Admission routes2
Has abstractyes

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