Resilience of Industrial Buildings to Wildland-Urban Interface Fires
Bibliographic record
Abstract
Abstract Forest fires have affected the Wildland-Urban Interface (WUI) in the last years with greater violence, causing damage to houses, industrial and commercial buildings. Businesses face the same fire hazard as houses but with a much greater potential due to labour interruption, job losses, and tax revenue impacts. In Canada, forest fires affected oilsand camps in 2011 and 2016, disrupting oil and gas production and damages of billions of Canadian Dollars. Camp Fire in 2018, the most destructive forest fire to date in the United States, destroyed houses, commercial buildings and other structures. In Portugal, the 2017 Large Forest Fires caused damages to almost 600 industrial buildings in the WUI and million euros losses. Assessing post-fire damage provides valuable information for future planning, fire risk mitigation, and improving fire resilience in the built environment. Considering WUI fires’ economic and social impact in industrial areas is essential to better understand this particular situation. In this sense, following the forest fires of 2017 in Portugal, a field research was conducted to assess the type of industrial building damages endured and know which construction systems were employed in their repair. The assessment considered the type of structure, materials used in facades and roofs, type of openings, ground waterproofing of uncovered areas, and material storage usage. There was also a verification of the materials used after the rehabilitation. This last stage concludes that building fire regulation needs to change for mitigating the risk of industrial buildings getting destroyed by Forest Fires in the WUI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".