Extreme blocking ridges are associated with vegetation fire occurrence in England
Bibliographic record
Abstract
Background Persistent positive anomalies (PPAs) in 500 hPa geopotential heights are an event-based paradigm for tracking large scale atmospheric patterns that often correspond to blocking events. Aims Examine the importance of PPAs for surface fire weather across the United Kingdom (UK) and vegetation fire occurrence in England. Methods We used linear regression models and lead-lag statistics to analyse relationships between PPAs and gridded surface weather, and we quantified landcover and season-dependent relationships between PPAs and vegetation fire occurrence and size using a comprehensive fire occurrence database. Key results Surface fire weather is more extreme under PPAs, characterised by reduced precipitation and anomalously high temperatures. Overall, 34% of England’s burned area occurs during or up to 5 days following the presence of a PPA. The percentage of PPAs associated with vegetation fires increases with increasing fire size, with PPAs being associated with half of fire occurrences >500 ha. Conclusions PPAs are associated with elevated surface fire weather and vegetation fires. They are especially important for larger fires in heathland/moorland and grasslands. Implications Synoptic-scale indicators of fire occurrence like PPAs may improve longer-term fire weather forecasts beyond surface fire weather indices alone, aiding vegetation fire preparedness and management decision-making.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".