Priority research directions for wildfire science: views from a historically fire-prone and an emerging fire-prone country
Bibliographic record
Abstract
Fire regimes are changing across the globe, with new wildfire behaviour phenomena and increasing impacts felt, especially in ecosystems without clear adaptations to wildfire. These trends pose significant challenges to the scientific community in understanding and communicating these changes and their implications, particularly where we lack underlying scientific evidence to inform decision-making. Here, we present a perspective on priority directions for wildfire science research-through the lens of academic and government wildfire scientists from a historically wildfire-prone (USA) and emerging wildfire-prone (UK) country. Key topic areas outlined during a series of workshops in 2023 were as follows: (A) understanding and predicting fire occurrence, fire behaviour and fire impacts; (B) increasing human and ecosystem resilience to fire; and (C) understanding the atmospheric and climate impacts of fire. Participants agreed on focused research questions that were seen as priority scientific research gaps. Fire behaviour was identified as a central connecting theme that would allow critical advances to be made across all topic areas. These findings provide one group of perspectives to feed into a more transdisciplinary outline of wildfire research priorities across the diversity of knowledge bases and perspectives that are critical in addressing wildfire research challenges under changing fire regimes.This article is part of the theme issue 'Novel fire regimes under climate changes and human influences: impacts, ecosystem responses and feedbacks'.
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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 | 0.060 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.012 | 0.033 |
| 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".