Citizen science as a tool to investigate landscape controls on fuel moisture and flammability
Bibliographic record
Abstract
Temperate heathlands are experiencing a growing number of wildfires. These ecosystems are dominated by the heather shrub Calluna vulgaris , the moisture content and flammability of which strongly influences wildfire ignition and behaviour. Understanding the drivers of these characteristics will help develop management strategies for fire-prone ecosystems. Field measurements of fuels are difficult to achieve over a large spatial scale. Citizen science may therefore be a valuable tool to gather such data. 134 volunteers, many with professional backgrounds in environmental topics, collected 713 samples of live Calluna canopy during a period of warm, dry fire weather from regions across the UK. The moisture content and flammability of submitted samples were calculated in university laboratories and mapped by region. Boosted regression trees were employed to determine the relative influence of a number of weather, phenological and landscape variables on fuel characteristics. There was regional variation in fuel characteristics. Fuel moisture content was generally lower across eastern regions and higher in southwestern regions of the UK. Flammability was highest in the East Midlands region. Fuel moisture content and flammability were associated most with Normalised Difference Vegetation Index (NDVI) and soil type. Citizen science has allowed us to investigate patterns in fuel characteristics across a larger spatial scale than would otherwise be possible. Continuing such research, and reaching a wide community of people with a diverse range of backgrounds, interests and wildfire awareness, is key in developing communities with a greater knowledge of wildfire risk, and will help us to develop wildfire mitigation and management strategies.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".