Validating a landscape metric to map fire exposure to hazardous fuels in Portugal
Bibliographic record
Abstract
Abstract Assessing wildfire hazard at the landscape level in Portugal with low-cost and time-saving methods is necessary to guide land and fire managers and protect communities at risk. This study applied a landscape metric developed in Canada to map wildfire exposure in Mainland Portugal between 1995 and 2018. The wildfire exposure metric was computed and validated by examining exposure within subsequently burned areas over five years: 1995, 2007, 2010, 2015, and 2018. Landscape fire exposure was computed by the proportion of neighborhood cells in a 100 m resolution grid that include hazardous fuel types. The resultant exposure metric analyzes the amount of land cover type in the area of a site that will either aid or prevent fire spread. The distribution of exposure levels remained relatively stable over time, decreasing just 0.5% from 1995 to 2018. Approximately 80% of burned areas occurred in sites with substantial exposure (i.e., ≥ 80%). This exposure metric, originally developed in Canada, aligned well with wildfires modulated by Portuguese climate and vegetation, leading to its successful validation. This study uses a simple, time-saving method to show high and low wildfire exposure areas, allowing managers to plan mitigation efforts at different scales, with potential applications for other countries facing large wildfire events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".