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Record W2062681419 · doi:10.3354/cr031085

Potential impact of climate change on fire risk in the Mediterranean area

2006· article· en· W2062681419 on OpenAlexaboutno aff
Marco Moriondo, Peter Good, Rita Durão, Marco Bindi, Christos Giannakopoulos, João Corte‐Real

Bibliographic record

VenueClimate Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationCommonwealth Scientific and Industrial Research OrganisationUniversity of Southampton
KeywordsClimate changeMediterranean climateGeographyMediterranean areaEnvironmental scienceClimatologyForestryEcologyArchaeology

Abstract

fetched live from OpenAlex

In this study, output of the Hadley Centre Regional Circulation Model (RCM) (HadRM3P, 0.44° × 0.44° resolution) was used as input to the Canadian Forest Fire Weather Index (FWI) for the present and 2 future IPCC climate scenarios (Special Report on Emissions Scenarios [SRES], A2 and B2 scenarios).The aim was to investigate the effects of climate change on fire risk (number of days with fire risk, length of fire risk season, etc.) for the EU Mediterranean countries.Results indicated a general increase in fire risk in both future scenarios over the whole study area.The increase in fire risk was mainly due to 3 components: (1) increase in the number of years with fire risk; (2) increase in the length of the season with fire risk; (3) increase of extreme events (e.g. total number of days with FWI > 45 and episodes with FWI > 45 for 7 consecutive days) during the fire season.As expected, A2 scenario showed a greater increase in risk than B2 scenario.These general increases in fire risk may have a very strong impact in areas where forest land cover is high (e.g. the Alps region in Italy, the Pyrenees in Spain and mountains of the Balkan region).

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)0.003
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
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 head0.051
GPT teacher head0.347
Teacher spread0.296 · 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

Citations499
Published2006
Admission routes1
Has abstractyes

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