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Record W2126338820 · doi:10.1139/x05-253

Effective heat of combustion for flaming combustion of conifers

2006· article· en· W2126338820 on OpenAlexvenueno aff
Vytenis Babrauskas

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsCombustionHeat of combustionEnvironmental scienceCharWater contentMoistureAtmospheric sciencesChemistryMeteorologyEngineeringGeologyPhysicsGeotechnical engineering

Abstract

fetched live from OpenAlex

The heat of combustion of burning trees is often used in forest-fire hazard modeling to relate mass-loss results to the heat produced; therefore reliable values are needed. Experimental results for the effective heat of combustion of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees are presented as a function of moisture content. It is also shown that during a forest fire, the effective heat of combustion does not correspond to the oxygen-bomb-test value. Instead, the value will always be lower, since the heat of combustion of char is higher than that of the pyrolysate vapors, and char mostly remains unburned during a forest fire. These are the first and only experimental results obtained from testing of actual trees. But results from benchmark testing and studies on wood products by other investigators are broadly consistent with our findings. It is further shown that moisture content has a major effect on the effective heat of combustion. A quantitative expression for the effective heat of combustion, as a function of moisture content, is obtained. Benchmark testing by earlier researchers established that generally there is only a slight species effect on the heat of combustion; therefore the present Douglas-fir results can be applied in more general forest-fire modeling.

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)0.003
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 score0.122
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.274
Teacher spread0.258 · 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

Citations83
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicFire effects on ecosystemsFrench-language works237,207