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Record W4293126195

Characterization and mapping of wildland-urban-interfaces: a methodology applied in the case study area in Sardinia

2007· preprint· en· W4293126195 on OpenAlexaboutno aff
C. Lampin, M. Jappiot, M. Long, D. Morge, Céline Bouillon, Luis Andrés Cucarella Galiana, G. Herrero, J. R. Solana, A. Mantzavelas, T. Lazaridou, T. Partozis, G. Loddo, Gabriele Delogu, S. Brigalia, G. Dettori

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2007
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Planning and Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsCharacterization (materials science)Computer scienceRemote sensingEnvironmental scienceGeographyMaterials science
DOInot available

Abstract

fetched live from OpenAlex

Large areas initially consisted of contiguous forests, particularly in Europe, have been influenced by human activities to a large extent. This influence contributed to the fragmentation of the rural landscape and forested areas were found surrounded by or intermixed with urban development. Urban and economic development in or near wildland vegetation poses a major threat to the environment (Johnson 2001, Radeloff et al. 2005). They are areas of humanenvironment conflicts, such as the destruction of homes by wildfires, natural habitat fragmentation, introduction of exotic species, and biodiversity decline (Radeloff et al. 2005). These areas that characterized of increased human activities and land use conversion make up the Wildland/Urban Interfaces. The significance of Wildland/Urban Interfaces (WUIs) has grown in recent years mainly because WUI, as a landscape unit, has grown worldwide (Steward et al. 2003). Essentially in USA, Canada and Australia interest for WUI study appeared after the huge fires of 1985 in the WUI (Davis, 1990) and in Europe this interest appeared ten years ago with increasing of damages on goods and people due to WUI fire. Large efforts aiming at the identification and mapping of existing or potential wildland urban interface areas have already been recorded in North America but they have to be developed in Europe. In the European project Fireparadox, several research and engineering teams are working together with a view to develop a methodology to characterize and to map WUI through spatial analysis of the territory according to regional and local approach. The objective at term is to be able to assess and to map vulnerability level or index according to each WUI types and also damages in case of fire. Theses results will allow to develop appropriated prevention actions, to help firefighters adapting their fighting strategy (concentration or dispersal of crews and facilities according to the stakes, positioning on the most vulnerable zones of interface ). The presentation presents an application of the method of WUI characterization and mapping on a case study area in Sardinia.

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)0.015
metaresearch head score (Gemma)0.001
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 score0.277
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
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 science0.0000.001
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 head0.070
GPT teacher head0.284
Teacher spread0.213 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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