On the need of a European program for wildfire-prepared communities – the FIREPRIME project
Bibliographic record
Abstract
Wildfires in the Wildland-Urban Interface (WUI) are a rising problem in Europe, driven by lengthening hot, dry seasons in southern regions and the emergence of fire-prone zones in central and northern countries unprepared for large-scale wildfires. Climate change intensifies these challenges, underscoring the urgent need to enhance resilience and self-protection capabilities of WUI communities.Although several EU initiatives have focused on improving community resilience to wildfires, their practical implementation and impact remain limited. These efforts are often isolated and localized, lacking integration into a cohesive, harmonized European strategy. This gap has left Europe without a unified framework for fostering fire-adapted communities capable of coexisting with wildfires. In contrast, international programs like FireSmart Canada and Firewise USA provide successful examples of global, community-centered approaches that could inspire European efforts.The FIREPRIME project aims to address this gap by establishing the foundations for an EU-wide program to promote a culture of wildfire resilience among WUI communities, with a focus on civil protection. FIREPRIME is designing at pilot level the program architecture and governance, and is developing a comprehensive toolkit of resources that includes a smartphone app, guidelines, checklists, and educational materials aimed at enhancing wildfire resilience in three critical targets: households, communities, and infrastructure.These tools are being piloted in three diverse European regions, each representing unique fire regimes, ecosystems, and population profiles: Collserola-Barcelona, Spain (Mediterranean Europe); Tyrol, Austria (Central Europe); and Gothenburg, Sweden (Northern Europe). This presentation will showcase the rationale behind FIREPRIME, its key tools, and initial results from pilot region collaborations, emphasizing the project's inclusive and regionally sensitive approach, which fosters active engagement with local stakeholders and WUI communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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 source (direct Gemma or distilled Codex), 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".