Characterization and mapping of wildland-urban-interfaces: a methodology applied in the case study area in Sardinia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".