Relationships between synoptic weather patterns, surface fire weather and vegetation fire in a temperate region
Bibliographic record
Abstract
Background Vegetation fire risk is increasing in temperate regions like the UK, yet understanding of surface and synoptic weather controls on fire is limited. Aims We examined seasonal relationships between (i) synoptic weather patterns and surface fire weather, (ii) surface weather and vegetation fire, and (iii) synoptic weather patterns and vegetation fire in England using a comprehensive fire database. Methods We used ranked percentile curves and relative difference metrics to address our three objectives. Key results Extreme surface fire weather is predominantly associated with high-pressure systems. The best surface weather predictors of fire are relative humidity (RH), fire weather index (FWI), fine fuel moisture code (FFMC) and initial spread index (ISI). Vegetation fires are strongly associated with high-pressure synoptic weather on the day of, and the week before fires in spring, but much less so in summer. Conclusions Persistent high-pressure synoptic weather is required to sufficiently elevate surface fire weather for vegetation fires in spring. Summer fires are less dependent on the specific synoptic weather pattern and extreme summer surface fire weather. Implications Incorporating both synoptic and surface fire weather may help to capture seasonal differences in the drivers of vegetation fire and provide opportunities for longer-term forecasting of elevated fire weather.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".