WUI guideline for Norway : Veileder for å beskytte bebyggelse mot skogbrann i Norge
Bibliographic record
Abstract
WUI guideline for Norway Norway is a long country where forests, grass, and heather cover vast areas. Approximately 38% of the country's land area consists of forests, and many structures are located near or surrounded by nature. In these wildland-urban-interface (WUI) areas, a wildfire could damage structures and infrastructure. Norway's tradition of constructing houses and cabins from timber adds an extra layer of vulnerability in WUI areas. As part of the EU-funded research and innovation project TREEADS, Norway's first WUI guideline has been developed to strengthen resilience against wildfires. The guideline is targeted at citizens in WUI areas, and presents measures that may protect built areas from wildfires. The development of the guide is based on an extensive process, including a literature review of WUI guidelines from countries such as the USA, Canada, and Sweden. This review formed the foundation for a list of relevant topics and recommendations, which were further refined through in-person workshops with stakeholders, surveys, and expert consultations. To ensure relevance for Norwegian conditions, the recommendations were adapted to local building traditions and by using insights from past fire incidents, fieldwork, and laboratory experiments. This process resulted in six main recommendations and five supplementary recommendations).
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.026 |
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".