Applying a Fire Exposure Metric in the Artificial Territories of Portugal: Mafra Municipality Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Portugal’s increasing wildfire frequency has led to home destruction, large areas burned, ecological damage, and economic loss, emphasizing the need for effective fire exposure assessments. This study builds on a Canadian approach to wildfire exposure and evaluates wildfire exposure in the Portuguese municipality of Mafra, using artificial territories (AT) as a proxy for the wildland–urban interface (WUI) and integrates land use land cover (LULC) data with a neighborhood analysis to map exposure at the municipal scale. Fire exposure was assessed for three fire transmission distances: radiant heat (RH, <30 m), short-range spotting (SRS, <100 m), and longer-range spotting (LRS, 100–500 m) using fine resolution (5 m) LULC data. Results revealed that while AT generally exhibited lower exposure (<16% “very high” exposure), adjacent hazardous LULC subtypes significantly increase wildfire hazard, with up to 51% of LULC subtypes classified as “very high exposure”. Field validation confirmed the accuracy of exposure maps, supporting their use in wildfire risk reduction strategies. This cost-effective, scalable approach offers actionable insights for forest and land managers, civil protection agencies, and policymakers, aiding in fuel management prioritization, community preparedness, and the design of evacuation planning. The methodology is adaptable to other fire-prone regions, particularly mediterranean landscapes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it