Global fire challenges in a warming world:
Bibliographic record
Abstract
Catastrophic wildfires are increasingly common across the globe. However, fire plays also a necessary and useful tool for food security, preservation of cultural landscapes and associated ecosystems. Global fire activity is shaped by diverse social, economic, and natural drivers, which determine the likelihood of a landscape to burn. The effects of climate change associated to other planetary changes are transforming fire activity in ways that it will likely be dramatic, with potential consequences to nature and society in case of adaptation failure. Based on the limited available statistics, there is a growing trend in the costs of wildfires. The key to wildfire disaster risk reduction in a changing world now lies in learning to live with fire. Our analysis revealed the following key issues for landscape management and governance: (i) Climate change in combination with other environmental changes linked to population growth and unsustainable land-use practices, is contributing to extreme wildfire events that exceed existing fire management capacities; (ii) Fire is an inherent feature of the Earth system and many ecosystems, are dependent on it for their long-term survival; nevertheless, ongoing changes in global fire activity in terms of location, intensity, severity, and frequency will likely have immense costs to biodiversity, ecosystem services, human well-being and livelihoods, and national economies; (iii) Engagement with local communities, land-owners, businesses and stakeholders is crucial to restore and maintain landscapes that are biodiverse and functional, respectful of local cultures and identities, economically productive, and above all, fire-resilient; (iv) People have historically achieved sustainable co-existence with flammable ecosystems and have often used fire as a land-management tool, thereby shaping many modern and long-standing landscapes around the world. Traditional fire knowledge is thus key to adapting to local changes in fire activity; (v) Building adaptive capacity to confront fires must be based on knowledge of the natural and cultural roles of fire, how they have shaped our modern landscapes, and their importance in the long-term functioning of socio-ecological systems; (vi) Catastrophic fires are part of our future. Current scientific estimates are likely conservative, meaning that changes in fire activity might likely be worse than anticipated.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".