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The role of the fuel moisture content on the prediction of large wildfires using the Fire Weather Index system

2022· book-chapter· en· W4312922330 on OpenAlexaboutno aff
Daniela Alves, D. X. Viegas, Miguel Almeida, Luís Reis, Jorge Raposo, Carlos Ribeiro

Bibliographic record

VenueImprensa da Universidade de Coimbra eBooks · 2022
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceWater contentMeteorologyMoistureIndex (typography)Scale (ratio)Range (aeronautics)Atmospheric sciencesGeographyEngineeringComputer scienceCartographyGeology

Abstract

fetched live from OpenAlex

Fuel moisture content is one of the fundamental parameters in forest fire research and management given its implications for many aspects of fire danger systems. In Portugal, such as in many other countries, to classify the days with more favourable conditions for wildfires it is common to use the Canadian Forest Fire Weather Index System (CFFWIS) which is based on the estimation of moisture content of several fuel components. The mathematical structure of CFFWIS requires as input the daily meteorological parameters which are used to estimate the moisture content of the soil for different layers that are the primary outputs of the system – the fire moisture codes. The final output parameter of the system is the Fire Weather Index (FWI) which represents a measure of the fire danger due to meteorological conditions. The temporal scale of the study is from 2018 and 2021 and the study area is in Lousã, a central region of Portugal. In this work, in addition to the meteorological data, we will use as input the direct measurements of dead fuels in the CFFWIS and analyse its influence on the FWI. The fuel moisture content (mf) is determined through the sample collection of dead pine needles in Lousã. For this temporal scale, mf by sampling is significantly lower than the modelled mf using meteorological parameters. An advantage of using the mf measurements is to increase the range of FWI variation, giving a higher sensitivity to the index to more easily discriminate the days with high fire danger and large burned areas. Two methods are addressed: “FWI a” which represents the traditionally FWI determined only by meteorological parameters, and the “FWI b” which is determined with fuel moisture content measurements and with meteorological data for the days that we did not have measurements. The original FWI, based only meteorological parameters, is compared with the FWI determined using mf measurements. The different methods will be related with the number of fires and burned area to analyse their performance. The results show a good fit between mf and FWI for days with extreme weather conditions (mf<5%).

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.184
Teacher spread0.172 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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